{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5.1</th>\n",
       "      <th>3.5</th>\n",
       "      <th>1.4</th>\n",
       "      <th>0.2</th>\n",
       "      <th>Iris-setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   5.1  3.5  1.4  0.2  Iris-setosa\n",
       "0  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "1  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "2  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "3  5.0  3.6  1.4  0.2  Iris-setosa\n",
       "4  5.4  3.9  1.7  0.4  Iris-setosa"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df = pd.read_csv('iris.data')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_len</th>\n",
       "      <th>sepal_wid</th>\n",
       "      <th>petal_len</th>\n",
       "      <th>petal_wid</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_len  sepal_wid  petal_len  petal_wid        class\n",
       "0        4.9        3.0        1.4        0.2  Iris-setosa\n",
       "1        4.7        3.2        1.3        0.2  Iris-setosa\n",
       "2        4.6        3.1        1.5        0.2  Iris-setosa\n",
       "3        5.0        3.6        1.4        0.2  Iris-setosa\n",
       "4        5.4        3.9        1.7        0.4  Iris-setosa"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=['sepal_len', 'sepal_wid', 'petal_len', 'petal_wid', 'class']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split data table into data X and class labels y\n",
    "\n",
    "X = df.ix[:,0:4].values\n",
    "y = df.ix[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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JM2fOpK6u9wPqxo8fz6pVq7j55puZMmUKl1xySd7jL7/8cm655RZ27NhBJBLhD3/4A/ff\nfz8ATz31FAsXLjx4r2symcTr9eZt+5xzzuH+++/na1/7Gueddx7Tpk3r1dYf//hHPvvZz3LssccC\nEAh03Rrw0ksv8bGPfYzjjz8egJtuuok777zz4BD1AX/605+46qqraGhoAOBLX/oSn/70pw/uv+SS\nS4Z7QgFdcymeP2Tb/92//ZGBDp49ezbNzc3liEukqlzXJRrNEgw2EwhEKtJmMhknGm0b8i1HtUB9\nxOD6iPvvv7/HObStrY1Jkyb1e8xgKakQkZoUieTvXMeMGcPKlSt58cUXef7557n11ltZunQp3/zm\nN3n55ZcxxvCrX/2KM844g0996lP88pe/5JhjjuGCCy7ocZXmt7/9Le973/sGbPucc85hyZIlvPDC\nC/zud7/jG9/4BkuWLCntiz3EoffD9vVe1BpjTBh4H3DgBZxijBkP7LXWbgKih5RPA9uttWsrG6lI\n7QkEIoTDjQMXLBGnsDuOapb6iPzxVNOg16koZHEjY8x3jDFbjTEJY8zzxpj8vxURqTnJRJLOfZ0l\n+5dMJAtu+9CrM/n2bdnStbTBpZdeyj333APApk2beOCBB1i8eDFtbW2cccYZAFx77bU88sgjzJkz\nh+uuu+5gXZ/4xCf44Q9/SDbbdbtBe3s7b731Vt52N2zYQDgc5sorr+TBBx9k7dq1xOPxHmUuu+wy\nHnvsMbZv3w503c7gOA5Tp05l3rx5B7f/9Kc/5YILLujVIVx44YU88cQTB+t9+OGHufjiiwt4x2rO\nWcBi4A261qm4F2gDvt1H+b5/4SJSk9RH9KQ+4l1DGak4sLjRz4HfHbrTGHMbcDNwDbAB+C7wnDHm\ndGttZWchiUjBfD4fTcEmopuiBU+aK1RTsAmfzzdguQMn0nxPrjiwbdmyZdxxxx0AZDIZrrnmGsaO\nHZu3vrPPPpv6+nrefvttLrroooPbZ8+ezW233caECROoq6vD6/Vy11138d73vrdX2/Pnz+e+++6j\nvr6ebDbLPffcc3AI+oDzzjuPb33rW1x88cUYY/D7/Tz55JOcccYZ3H333Qe3jx49mv/5n//pFee0\nadNYsWIF55xzTo9JeMONtXYBg7hYNdA8ChGpHeoj1EcMxPSX9Q14sDE54BPW2qe7bdsK3G2tnb3/\n51HADuBz1ton8tTRDLzxxhtv6H7ZQYjFYjz++Ms0NU0paLi0szNGNPoyV189pddkRxmZDtxP2f2z\np4WNhqd8v8tu98tOsta2VTXAIqiPkIHEYjFefvxxpjQ10Rgu3VOJYp2dvByNMuXqq8vabw62Py+F\ngb4T5DungPqI4aiv32U5+oiSzqkwxowBjgf+dGCbtbbDGLMI+DDQK6kQkdoRDAZ1YhcRkbzUR0h/\nBj2nYgDH03WP7I5Dtu/Yv09ERERERA4zevqT1JxSDa9qOFVERESkMkqdVGyn61GCx9FztOI4up4I\n0ictbCSwfxGgZ1uJOtGBCw+gKdjE9I9NV2IhI0b3xY3KsbCRiIhIX0qaVFhr1xtjtgMXAH+HgxO1\nJwM/6u9YLWwksH8RICdKcHSQQCgw5HqSiSTRTdFhvbCPyGB1X9yoHAsbiYiI9GXQSUUBixvdD3zd\nGLOOrkfK3glsBlpLErGMCIFQgHBDcU/xKPUj70YCPdlDRET6oj5C+jOUkYqzgJfompB9YHEjgEeB\n66y1dxljQsDDwBHAn4GPaY0KkdrmOA4vtbaSjRZ/69mh6puamDp94FvRxowZQ2trK+PGjeuxfdu2\nbcyYMYMFCxaUJJ5vfetbnHbaaUO6vfILX/gCM2fO5Pzzz++33MMPP0w8HuerX/3qUMMUEakZ6iMK\nM5L7iEEnFYUsbmSt/S/gv4YWkohUg+u6ZKNRmoNBIoGh33p2qHgySVt06LeiZbNZTjjhhJJ1FgDf\n/nb+BZ4PrNuTb2GlA/ItTJTPF7/4xcEHJiJSo9RHqI8YSKkfKSsiw1wkEKAxHC7Zv8F0PgdO1FOn\nTuWWW27h3HPP5eKLL2bjxo00NTUBkEwmmTFjBmPHjmXixIlMmzYtb10XXXQRv/vd7w7+PH/+/INz\nDD7/+c/z4IMPAl2dx5VXXsm0adM488wz2b59O3/5y1+YOHEi48eP5/rrr2fChAm8/PLLB2N7+umn\nD9Zz4403cuGFF3Lqqady5ZVXkslkDtY7a9asg+3/8Ic/ZNy4cUyYMIFzzz2XZDLJjh07+OhHP8rZ\nZ5/NmWeeyVe+8pWC3ysRkWpQH6E+oi96pKyI1KS1a9fyyiuvUFdXx8aNGw92JvPmzSMWi7F8+XIA\n2tvb8x7/+c9/nkceeYQrrrgCgDlz5nD99dfnLbtw4UKWLFnC0UcfTTqdZvLkyTz22GNMmTKF+fPn\nM2fOnD7jXLp0KfPnz8fn83Heeefx29/+lquuugp4twN89NFH+f3vf89f/vIXIpEIsVgMv99PU1MT\nf/jDHwiFQuRyOaZPn84TTzzBpz/96SG9ZyIiI4X6iNqjkQoRqUkzZ86krq73KWr8+PGsWrWKm2++\nmSeeeAKPJ/+1kcsvv5xFixaxY8cOOjs7+cMf/sDVV1+dt+wll1zC0UcfDcCbb76J1+tlypQpAPzz\nP/8zp5xySp9xXn755fj9fowxfOhDH+Ktt97qVeZ///d/ufHGG4lEIgA0NjZijCGbzXLrrbcyYcIE\nJk6cyBtvvMGSJUv6f2NERER9RA1SUiEiNenAyfVQY8aMYeXKlUybNo1XX32VsWPHEovFuOWWW5g4\ncSLNzc2sWLGCQCDApz71KX75y1/ym9/8hgsuuODgGg6FtnVAf/fPBroN3dfX1x8c2i7Efffdx65d\nu3jttddYunQpLS0tJJPJgo8XERmp1EfUHiUVIlIzDkyC62/fli1bALj00ku55557ANi0aRMPPPAA\nixcvpq2tjTPOOAOAa6+9lkceeYQ5c+Zw3XXXFRTDqaeeSjqdPnh/7IIFC/JeWRqMj3/84/z0pz+l\no6MD6FqYLpfLEY1GOf744/F6vWzfvp3f/OY3RbUjInI4Ux9R232E5lSISA/xEl8FGUx9B6725Lvq\nc2DbsmXLuOOOOwDIZDJcc801jB07Nm99Z599NvX19bz99ttcdNFFverKx+fz8etf/5ovf/nL5HI5\nJk2axKmnnnrwClb3Y/urp7uZM2eybds2zj33XDweD5FIhBdeeIFbbrmFK6+8kjPPPJMTTzyRf/mX\nfymoPhGRalEfoT6iL0oqRAToOlHWNzXRFo2CU9qFA+ubmvD5fAOWe/vttwF48cUXe2z/x3/8R/bu\n3QvAtGnT+nyaRz7Lli3rte0Xv/jFwf9/61vf6rV/3LhxLF68GIDXX3+d5557jg984AO9YuteD8Dd\nd9/dZ71f+9rX+NrXvtZj2+jRo1m0aFGhL0VEpGrUR7xLfUR+SipEBIBgMMjU6dO1Wirw29/+ltmz\nZ2Otxev18thjj/W4L1ZEZKRRH/Eu9RH5KakQkYOCweCwOrGXy+c+9zk+97nPVTsMEZGaoj6ii/qI\n/DRRW0RESsIYc54x5mljzBZjTM4Y8/Fu+zzGmB8aY/5ujInvL/OoMeaEasYsIiKloaRCZISqr68H\nKMtQtlTWgd/hgd9pFYWBJcBNwKGPaQkBE4BvAxOBy4FTgdZKBigiA1P/cPioZP+g259ERqiTTz6Z\nQCDAd77zHb75zW8WNElOao/runznO98hEAhw8sknVzUWa+08YB6AOeSxJ9baDuDi7tuMMTcDi4wx\n77HWbq5YoCLSL/UPh4dK9w9KKkRGqMbGRlpbW5k+fTrPPvtstcORIgQCAVpbW2lsbKx2KIN1BF0j\nGu3VDkRE3qX+4fBRyf5BSYXIMOU4TtFD05MnT2b16tW88847ZLPZXvvj8Tgr58/nQ0ceSUMZJ+ft\ncxwWd3Qwcdo0GhoaytbO4ai+vp6TTz552CUUxhg/8APgcWttvNrxSE+lOL/kM9ye8jOSXXTRRWzf\nvp0NGzbk7R+k9lW6f1BSITIMOY7DS62tZKPRsrbT6Thk9u7l9LFjOfbII8vWTqyzk45olAkTJgy7\nL8cyeMYYD/AbukYpbirkmFmzZvX622hpaaGlpaX0AY5w5Ty/1Dc1MXX6dCUWw0RjYyPjx4+vdhhS\npLlz5zJ37twe22KxWMnbUVIhMgy5rks2GqU5GCRSxmdjb7WWdYkEGV2lkhLpllCMBj5a6CjF7Nmz\naW5uLmts0qVc55d4MklbNIrrukoqRCoo3wWYtrY2Jk2aVNJ2lFSIDGORQIDGcLhs9ccSibLVLSNP\nt4TiFGCqtba8Q21SlLKcX0q8ErOI1A4lFSIiUhLGmDDwPuDAk59OMcaMB/YC24Df0vVY2UsBrzHm\nuP3l9lpr05WOV0RESkdJhYiIlMpZwEt0zZWwwL37tz9K1/oUl+3fvmT/drP/56nAyxWNVERESkpJ\nhYiIlIS1dgH9L6qqBVdFRA5TOsGLiIiIiEhRlFSIiIiIiEhRlFSIiIiIiEhRlFSIiIiIiEhRlFSI\niIiIiEhRlFSIiIiIiEhRlFSIiIiIiEhRlFSIiIiIiEhRSr74nTGmjq6VUz8DHA9sBeZYa79b6rZE\nRESkvFKpFJlMpuh6OhMJ3HR6UMc4joPruv3u6+joYM/evezOZnETiSHH5/F48Pl8eDwe/H7/kOsR\nGanKsaL27cAXgWuAlcBZwBxjTLu19qEytCciIiJlkEqlWPTGIuJuvOi6OlMplrmGf3IcGhsbByzv\nOA6trS8RjWZ77XPdFEtXL8LJxHHdFJl163jH5yPo9Q45Pl99kDH/8AGOOMLP5MlnDrkekZGqHEnF\nh4FWa+28/T+/Y4y5GvhQGdoSERGRMslkMsTdOL6j/fj8Q//CDpCOQeKd9j5HHg7lui7RaJZgsJlA\nINJjXyIRw/jfoXFMAJvL0ZlI0xgKEfQNbYQhnU6T3etS7zmJeHxbSUZmREaaciQVfwG+YIx5v7V2\nrTFmPPBPwKwytCUiIiJl5vN7CQQDRdXhdVJDOi4QiBAO9x7Z8PmCREY1AZALRQiGI4T8Q4vRTSVJ\ndsTxeQMM8g4tEdmvHEnFD4BRwJvGmCxdk8H/01r76zK0JVJ2/d3TO5jyPp+PYDBYkphisdig700W\nERERKZdyJBVXAVcDM+iaUzEBeMAYs9Va+6u+Dpo1a1aveyxbWlpoaWkpQ4gihXEch9ZnW4k60YLK\nu67L+jeW4ul0eu0LeUOMHzseXxH3/B7Q6TisX7GC86ZMgXC46PpkeJs7dy5z587tsS0Wi1UpGhER\nGYnKkVTcBXzfWvub/T+vMMacDNwB9JlUzJ49m+bm5jKEIzJ0rusSdaIERwcJhAYeVk/EE4xabRh/\nVCNBn+/g9rSbxt2T4sORCOFQqOi4tlrLukSCTLb3BEYZefJdgGlra2PSpElVikhEREaaciQVIeDQ\nbzo5tCaGDGOBUIBwQ2EjAr6Aj6ZRESLd7j9OOknicWgMhQiXYGQhVsRjE0VERERKrRxJxTPA140x\nm4EVQDNdk7T/TxnaEhERERGRKivH6MHNwJPAj+iaU3EX8BPgm2VoS0REaoQx5jxjzNPGmC3GmJwx\n5uN5ynzHGLPVGJMwxjxvjHlfNWIVEZHSKnlSYa3ttNb+u7V2jLU2bK19v7X2W9ZaPfRZROTwFgaW\nADcB9tCdxpjb6Lrw9K90rV3UCTxnjPEdWlZERIaXctz+JCIiI9D+RU/nARhjTJ4itwB3Wmv/sL/M\nNcAO4BPAE5WKU0RESk+Tp0VEpOyMMWOA44E/Hdhmre0AFgEfrlZcIiJSGkoqRESkEo6n65aoHYds\n37F/n4iIDGNKKkREREREpCiaUyEiIpWwHTDAcfQcrTgOWDzQwbNmzaKxsbHHtnyL/omISE9z585l\n7ty5PbbFYrGSt6OkQkREys5au94Ysx24APg7gDFmFDCZrkeQ92v27Nk0NzeXN0gRkcNQvgswbW1t\nTJo0qaTtKKkQEZGSMMaEgffRNSIBcIoxZjyw11q7CbifrsVR1wEbgDuBzUBrFcIVEZESUlIhIiKl\nchbwEl0Tsi1w7/7tjwLXWWvvMsaEgIeBI4A/Ax+z1rrVCFZEREpHSYWIiJSEtXYBAzwAxFr7X8B/\nVSIeEREBYRUKAAAgAElEQVSpHD39SUREREREiqKkQkREREREiqKkQkREREREiqKkQkREREREiqKJ\n2iOI66YGvdiJz+cjGAyWKSIRERERORwoqRghXNdh6dKVZLO5QSUJTU31TJ8+VYmFiIiIiPRJScUI\nkcm4OE4dgcBEmpqOLeiYZDJONNqG67pKKkRERESkT0oqRphAIEw43FhweccpYzAiIiIicljQRG0R\nERERESmKkgoRERERESmKkgoRERERESmKkgoRERERESmKkgoRERERESmKkgoRERERESmKkgoRERER\nESmKkgoRERERESmKkgoRERERESmKkgoRERERESlKWZIKY8yJxphfGWN2G2MSxpilxpjmcrQlIiLD\ngzGmzhhzpzHm7f19wzpjzNerHZeIiBTPU+oKjTFHAK8CfwIuBnYD7weipW5LRESGlduBLwLXACuB\ns4A5xph2a+1DVY1MRESKUvKkgq5O4x1r7Q3dtm0sQzsiIjK8fBhotdbO2//zO8aYq4EPVTEmEREp\ngXLc/nQZ8Lox5gljzA5jTJsx5oYBjxIRkcPdX4ALjDHvBzDGjAf+CfhjVaMSEZGilWOk4hTgS8C9\nwP9L1xWoB40xKWvtr8rQnoiIDA8/AEYBbxpjsnRd2PpPa+2vqxuWiIgUqxxJRR3wN2vtN/b/vNQY\nMxa4ERgRSYXjOLiuO6hjfD4fwWCwTBFVxlBe96FisRhpN12iiESqoxSfhUIMw/PGVcDVwAy65lRM\nAB4wxmzVRSepRSnXJRaLlbzeYfjZFRlQOZKKbcCqQ7atAq7o76BZs2bR2NjYY1tLSwstLS2lja7M\nHMehtfUlotHsoI5raqpn+vSpw/Yk4zgOrc+2EnWKm4/vJBxWrF7BlFOnECZcouhEKsdxHF5qbSUb\nLf+zKeqbmpg6fTpPPfUUc+fO7bGvHF+ESuAu4PvW2t/s/3mFMeZk4A4GuOh0uPQRMnw4rsvKpUvJ\nZbMl75sPfHaHa58vw8vcuXMr0keUI6l4FTj1kG2nMsBk7dmzZ9PcPPyfOuu6LtFolmCwmUAgUtAx\nyWScaLQN13WH7QnGdV2iTpTg6CCBUGDI9didlsTyBNnM4JIykVrhui7ZaJTmYJBIYOifhYHEk0na\nolFc18375bqtrY1JkyaVrf0hCgGHfrhzFDC/73DpI2T4cDMZ6hyHiYEAxzY1laze7p/d4drny/BS\nqT6iHEnFbOBVY8wdwBPAZOAG4AtlaKtmBQIRwuHGgQvu5zhlDKaCAqEA4YahjzAk4okSRiNSPZFA\ngMZwmUfbht+J4xng68aYzcAKoBmYBfyfqkYl0o9wOT7Lw++zKzKgkicV1trXjTGX0zUh7xvAeuAW\nTcQTERnxbgbuBH4EHAtsBX6yf5uIiAxj5RipwFr7R/SIQBER6cZa2wn8+/5/IiJyGCnHOhUiIiIi\nIjKCKKkQEREREZGiKKkQEREREZGiKKkQEREREZGiKKkQEREREZGiKKkQEREREZGiKKkQEREREZGi\nKKkQEREREZGilGXxOxEREZFDZTIZOjo6iMViA5aNxWI4Tid+f++yiUSMTCZdjhDJZNIkEgkSiQSZ\ndBonkaCzs7Nk9XcmEjiOc/A9iMVipNNuyeoXqRYlFSIiIlJ2mUyGLdu20PpiK0cedeSA5R3H4bU3\n1xIKb8XnC/bYl0o5bNq6grGnTMHj8ZcwRpf16zeRy1liKYf1G3aycNEamiI7StZGZyrJ35w4m/kL\nwWAYx+lkxYr1TJlyHuFwyZoRqTglFSIiIlJ2uWwWN+sSeE+AppOaBizvT/gJ7ooQDh+B3x/qsc9G\nLanNCbLZLJ4SfpPJZjO4rsHrO4WQJ43Pt5Vw6ANEIkeXrhFvgpBp54gjziUUasTarSQS68hmM6Vr\nQ6QKlFSIiIhIxfgDfsINBVySrwd/MIA/FCLg71k+5STKFF0Xn9dPFi8ejxefP0QgULohhAzgS6cI\nhRoJhxtJJAa+FUxkONBEbRERERERKYpGKmqE66YKmrh2QC1P7Eq5KTKZwQ3jZjNZ6k09iXgCN+WS\niCfoDA19YlwiniDWHmPTpk093tdsNkt9fX3B9XR0dNDR3oE/Xtg9u4l4gkw6/2vPZDIkEsVfXfOU\naKw/lRr491TIREWPx4PfX/w9zSnXHdRnYCh8Ph/BYHDggiIiIjIoSipqgOs6LF26kmw2V/AXnlqd\n2JVyU7yxZBXxeK7gYzJums51a/nAsUeS7EySWb2RzS8son1UZMhxOPEEa5asZdtfX8fr9QKQzmTY\ntnMXJx57TMFfzNNumq07tvL+9e8nFAkNWD6ZTLF73SbSZ4+Fbr/KTDrD+o3ryeVy+Hy+Ib2mAyK+\nCCeedEpRdaRSKRa9sYi4G++33K6OfazfsoGFSxfR1MfvI+KLMHnS5KISC8d1Wbl0Kblstqxf+uub\nmpg6fboSCxERkRJTUlEDMhkXx6kjEJhIU9OxBR1TqxO7spks8XgOv++9eH2Bgo6Jx/Zio3/nzBPr\nqW8IEfL7GNcQYlQRSUVHLoffA6eNHkUk0lXP7o59/HnHFiaf2EBjgXXHO+K8uWcr48KBguLZZS3z\nnRTZTLbH9uz+CYreo31EGob+utxUmvjuOJlsduDC/chkMsTdOL6j/fj83j7LdYZy+Db6CJ8QInJE\n77gPxpPJFJVUuJkMdY7DxECAY5sGnsA5FPFkkrZoFNd1lVSIiIiUmJKKGhIIhAmHGwsqW+sTu7y+\nQK+JdX1JebtuCQr6fXgtBL0ewgE/kWBhSUk+2WSKoNdD06gIjUd0vacZwOP10DgqwtFHFvY+e83g\n4ok7qX73+3xeAkW8LgCX/tsYDJ+//3j8TgqP14PP7++zXCnjCQcCNJZz6M1xyle3iIjICKaJ2iIi\nIiIiUhQlFSIiUjHGmBONMb8yxuw2xiSMMUuNMc3VjktERIqj259ERKQijDFHAK8CfwIuBnYD7wei\n1YxLRESKp6RCREQq5XbgHWvtDd22baxWMCIiUjq6/UlERCrlMuB1Y8wTxpgdxpg2Y8wNAx4lIiI1\nT0mFiIhUyinAl4DVwEXAT4AHjTGfrWpUIiJSNN3+JCIilVIH/M1a+439Py81xowFbgR+Vb2whsZx\nHFzXLUldWu1dRoJSfmYKpc9W5SipEBGRStkGrDpk2yrgioEOnDVrFo2NPdeXaWlpoaWlpXTRDYLj\nOLQ+20rUKc0c86ZgE9M/ptXe5fDlOA6trS8RjRa3eOtgNTXVM3361BH92Zo7dy5z587tsS0WK/16\nZ0oqRESkUl4FTj1k26kUMFl79uzZNDfXzpNnXdcl6kQJjg4SCBW3oGUykSS6Sau9y+HNdV2i0SzB\nYDOBQKQibSaTcaLRthH/2cp3AaatrY1JkyaVtB0lFSIiUimzgVeNMXcATwCTgRuAL1Q1qiIEQgHC\nDcWvAu+g1d5lZAgEIoTDjQMXLBFHH62K0URtERGpCGvt68DlQAuwDPhP4BZr7a+rGpiIiBSt7EmF\nMeZ2Y0zOGHNfudsSEZHaZq39o7V2nLU2ZK09w1r7i2rHJCIixStrUmGMORv4V2BpOdsREREREZHq\nKVtSYYyJAI/Rdb9se7naERERERGR6irnSMWPgGestS+WsQ0REREREamysjz9yRgzA5gAnFWO+oci\nlUqxefPmQR930kkn4fV6yxBRcdrbd7Bv31527txAJjPwow327t2at7zPF+Soo07s8zjXTRX0LONY\nLIbjOGQTWbKZTGEvQkREREQOCyVPKowx7wHuBy601qYLPa7cCxutXLmSP/1pC8YU/pKNyXDRRQ5j\nx44tSQyl4jhxlr81n83uShZvfo5w+6gBj+mMR9lyaHlridQdybnjP5n38W6u67B06Uqy2dyAz3d2\nnE5ee3Mtni1etu9q54wzPkjAP6SXJyKDVKmFjURERPpSjpGKScAxQJsxxuzfVg9MMcbcDPittfbQ\ng8q9sFEul8OYo/jABz5c8DFr1vyZbLayKz8WwtocadKEjh/FcaePoXHUUQMeE+vYxbbUhh7lU06C\nfW/txdpc3mMyGRfHqSMQmEhT07H91u/3xwiFt2K9llRqL7lc7b1vIoerSi1sJCIi0pdyJBUvAGce\nsm0OsAr4Qb6EQmpbIBAuaKEany9IzqdkQkRERGSkKXlSYa3tBFZ232aM6QT2WGtXlbo9ERERERGp\nrrJM1M5DoxMiIiIiNaLQB7GUSiwWI512K9aeVF5Fkgpr7Ucr0Y6IiIiI9G8wD2IpFcfpZMWK9UyZ\nch7hcEWalAqr1EiFiIiIiNSAwTyIpVSs3UoisY5sVo+dP1wpqRAREREZgQp9EEspJBJ6zPXhrpwr\naouIiIiIyAigpEJERERERIqipEJERERERIqipEJERERERIqipEJERERERIqipEJERERERIqipEJE\nRKrCGHO7MSZnjLmv2rGIiEhxlFSIiEjFGWPOBv4VWFrtWEREpHhKKkREpKKMMRHgMeAGoL3K4YiI\nSAkoqRARkUr7EfCMtfbFagciIiKl4al2ACJDlc6kyWazefelUikymQypVIpkKtm1zU2RzeUvP5xk\nMhmSjkMmncZJJOjs7Bx0HYlEgmymNO9FJpMhkUgUVUcikejzdymHF2PMDGACcFa1Y6mkVDJFJp3J\nuy8RTxBrj7Fp0yZisdig6vX5fASDwYK3i4iUi5IKGZbSmTSr12wg6di8+514gp07Y6xZs5VAqOvu\nir3xTvbuiZHOpCsZakll0hnWb1zPjlgH67dsYOHSRTSNigy6Hjfl8s6WdzjjH84gQKDoeHK5HD6f\nb8j17OrYx+atm0m77pDrkNpnjHkPcD9wobV2UB/EWbNm0djY2GNbS0sLLS0tJYywPFLJFCv/tIi6\njnje/U48wZola9n219fxer2DqjsTDjJm0vhen7+mYBPTPzZdiYWIMHfuXObOndtj22AvYBRCSYUM\nS9lslqRj8XpPoN7TuxO26Q483j34AycSCI4CwJPcQzqzHpvLVTrckslms7hZl8YmL74GH+ETQkSO\nGHxSsS8WJ7UxRS5b3HtxIB7v0T4iDYOP44B2Two365LJ5L+SK4eNScAxQJsxxuzfVg9MMcbcDPit\ntXmvFMyePZvm5uYKhVlamXSGuo44E/w+Qv7eyXdHLoffA6eNHkUkUvjnyHFdlqbSNIxpIBQJHdye\nTCSJboriuq6SChHJewGmra2NSZMmlbQdJRUyrNV7vPi8va+0Z7wp6us8eD3+g/vzJR/DldfnxeP1\n4PP7CQQHP9KQSqZKGo/P5x1SHAd4fYfP70b69QJw5iHb5gCrgB/0lVAcLkJ+H5E8n5NsMkXQ66Fp\nVITGIxrzHJlf3Eni64gTioQIN4R77HNwio5XRGQwlFSIiEhFWGs7gZXdtxljOoE91tpV1YlKRERK\nQU9/EhGRajqsRydEREYKjVSIiEjVWGs/Wu0YRESkeBqpEBERERGRoiipEBERERGRoiipEBERERGR\noiipEBERERGRoiipEBERERGRoiipEBERERGRoiipEBERERGRoiipEBERERGRopR88TtjzB3A5cBp\ngAP8BbjNWrum1G2JiIhIb67rEovFhnRsLBbDcRw6/X68QDaTLW1wNSqbyeC6DpmMSyrlkCJLJpPG\nTSVIJjsHVVd9vQev11+mSEVqUzlW1D4P+G/g9f31fx/4v8aY0621ThnaExERkf3cpMvS5UvJZrME\ng8FBH+8kHLYuew0TDuLF8M6WdzjjH84gQKAM0daGbDrDzl3rSTgd7IxvYM3mv5LI5dgZ3cCb7yxi\nVCgyqPoC9RFOPWWyEgsZUUqeVFhrL+n+szHmWmAnMAl4pdTtiYiIyLsymQxOxiEwOkDTUU2DPt4f\n99P+VohwQxjrpkltTJHL5soQae2wuSxpXDxHevFkfPiPC5PN5PDu9hE8JkSgofCkIuOmSe6Nk81m\nlFTIiFKOkYpDHQFYYG8F2hIREREgEAwQbggP6VhfwEcg6CdrShxUjav3eqn3ePD6/PjqctTVe6j3\n+fH5BzdKkyFVpghFaldZJ2obYwxwP/CKtXZlOdsSEREREZHqKPdIxY+BDwL/VOZ2ZJjLZjO4bopc\nrmvoPpVKkUwl+yyfSqXI5UbG5EERERGRWle2pMIY8xBwCXCetXbbQOVnzZpFY2Njj20tLS20tLSU\nKUKpFZmsy5497axevQ1PzrJzZ4w1a7YSCLX3fUwmza5dUU466STwVjBYkRo0d+5c5s6d22PbUJ/8\nIyIiMhRlSSr2JxTTgfOtte8Ucszs2bNpbm4uRzhS47LZDJkMeDzHEjD1eLx78AdOJBAc1ecxSSdO\nOr0Xaw/vyYMihch3AaatrY1JkyZVKSIRERlpyrFOxY+BFuDjQKcx5rj9u2LW2r7vZ5ERz1PvxVvv\npb7Og9fjx+fte2JcJq1JcCIiIiK1ohwTtW8ERgHzga3d/n26DG2JiMgwYYy5wxjzN2NMhzFmhzHm\n98aYD1Q7LhERKV451qko6xOlRERk2NLiqCIih6lKrFMhIiKixVFFRA5jGlUQEZFq0eKoIiKHCSUV\nIiJScVocVUTk8KLbn0REpBqqsjiq4zi4rlt0PbFYjLSbLkFExUmnMyTiiR7bEvEEbsolEU/QGeoc\ndJ2JeIJMOlOqEHvIZnM4nQ6d+waOK5FIkHKSeOsScMhapyknQSbtknISkOt6NHktyWYypFKJvPuS\nqQSdiQ52795EIhFjz54tdHa2s2fPFqD366iv9+H3Bwtq1+MpvOxgpFIOmUxxn5tEogPXTZJIdNDZ\nGQbKF293rpuq6Lo92WyW+vr6irUH4PP5CAbL+z4WQkmFiIhU1GAXR4XSLJDqOA6tz7YSdaKDCTd/\nXQmHFatXMOXUKYQJF13fUKTSabas3oDN5vAFfAe3x2NxMqs3svmFRbSPigy63mQyxe51m0ifPbak\ntzOk0hk6d7ezbf7rpFa8PWB513Vx1m4j59+L1+Prsc/pjON9ZyOd/kUkPV7imzeRPW0s+EsY8BBl\n0xl27lqPJYfnkLgBOpKdLH5nHes2vYbH4yWV6mT39vX8NbYWvz/Uq7y/PsTJ7xmPxzPwSq+5SBMf\nnDy9pF/UUymHlYtaqYsX97mJx/eS2biUzYvCtEeOAMoTb3eu67B06Uqy2VxFvnS7boq33lrN+953\nGl5v7999uTQ11TN9+tQ+X2OlFkhVUiEiIhUzlMVRoTQLpLquS9SJEhwdJBDqex2cQtidlsTyBNlM\nduDCZZLO5PA6Kcb7vTR1Sx46cjlCfh/jGkKMGkJSscta5jspspksdaZ08WayOYKZLON9XkYXEFcy\nlaIx6McfCOPz9swWnFyOY7w+Tg6FSGJ4w02RzVXvd9GdzWVJ41J/pI9AqPfr7NyXI7ITzjpuFOFw\nhGQywDt2JyeNPopAoGf5bDpNJpri9GCEQJ6Eo7uEm2RJPEom45b0S3om41IXjzLBFyTkG/rnpiOX\nJeQLMi7UyKhIU9ni7S6TcXGcOgKBiTQ1HVuWNrrbu3cru3a9yWmnjatIewDJZJxotA3XdftMKiq1\nQKqSChERqYhaWRw1EAoQbihudOHQW46qKejzEQm++2Uvm0wR9HoIB/w9thcq7pR3cdGAz1tQXJ66\nrtcW8OdZDDWVwu/xEPT5IVemQIvk9Xrx+Xu/Tl8qRV29h3A4whGjGkl4IBQIMCoyilCooUdZN5Uk\nGYeIP0QgUMDfrFu+JzOHfAEihcTQh2wqQdDjI+wPvVtPGePtLhAIEw43DlywSIlErKLtHeDUyAO5\nNVFbREQqRYujiogcpjRSISIiFaHFUUVEDl86wYuIiIiISFGUVIiIiIiISFGUVIiIiIiISFGUVIiI\niIiISFE0UXsAyWRyUAuExGIx0uniV2vtTzabYc+Wt0m2b2F7oI19hzyCLp/ORAxnwya2m3fLp90U\nzvY4y5J+vIc+rg/o6NjN7p0beqx+2ZdEIkYmk6ZOearUsJTrln1l1Vgshpuu/krLIiIilaSkoh/p\ndIr585fR1raz4GMcp5MVK9YzZcp5hMu0yGoqlSAQ3cWZiQTv7YgRdDMDx5XcR2PCYXS38mnXJRGL\n4e14kVxd70WDcvEouzp2sWjV74nsX/2y75gcNm1dwUnHjB3aixIpM8d1Wbl0Kblstqwrq3Y6DutX\nrOC8KVMo20lARESkxiip6Ecmk8ZxDMcd19xrlcu+WLuVRGId2ezAX/SLNcrv5/gjmnotlpNPIuEh\nsTfUo3w6laIjbgFL8LhReHzeHseYHVDviREa00ikoanf+m3UktqcIFeB1y0yFG4mQ53jMDEQ4Nim\n/v+ei7HVWtYlEmSytbG6r4iISCUoqShAIBApeGXEA6spDjceX++VP71eH/VeD/5giECo/yuuKad2\nVpcV6U84EKCxjCMIsYQ+CyIiMvIoqRARkZrnui6pVKqoOlKpFNj+y2TSGawdoBCQdtNkM1nSbho3\n5ZLJ9D1K63Q6ZDIZMtmuf4fKZrLkcjmy2Wze/XmZwoqJiFSKkgoREal5z770LCs3rCyqjkQiwa69\nu2g6Lf/tb7G9Md5+pQ1TwDy1fbF9dK7ewFvPL+TNbJpt2/oepXbdNNk97TT94wkE/b7ece1LsGNX\nlDdXv0MoHCrotdTVQeiIys/ZSWfSZId4a5/ruuRyWVzXJZlKDlg+lUqRy+k2wsFIZ9yC7phIJDpw\n3eSgHsQiMhAlFSIiUvPqj6vHM7q4LmvPqj0kkn3fnuZ0OgR2RTnzqP4fTAGwz1NPoK6OsfX1xJNJ\nmlJBIpH8ycq2zr2scVOYuhOpq/P32l9X10Edu6irO466ulEFvZZ4fBvecGW/6KUzaVav2UDSGXgk\nJ5+tO3bT2Zni7be3s2/fwIlbJpNm164oJ510EngHLD7ipdIuWzYsxeay+Hz9P4wiHt9LZuNSNi8K\n0z7Ag1iSKYfdm1aQHjsF0MMnpG9KKkREpOaNOmIUTUcXN8F+k3fTgGXqjOGoUQM/mMOTzdLg99HU\nEMJjcsQjhiOPPDZv2c50Fm+sg0AgRMDf+/HdOTeDx+Mj4A8RCBT2pa2zs/KP785msyQdi9d7AvWe\nwX/L9/s81NWtxuc7nkDwxAHLJ5046fRerM0NJdwRJ53L4E05jPcGaOojwT2gI5cl5AsyLtTIqAHK\n7rKW+anEkEeoZORQUiEiIiIFq/d48eVZ22ggXq8PY+rxenwFHZ9JFzeHZqQK+gJEBkhOs6kEQY+P\nsD80YNl4Sg+fkMJopTIRERERESmKkgoRERERESmKkgoRERERESmKkgoRERERESmKkgoRERERESlK\n2ZIKY8yXjTHrjTGOMWahMebscrVVSQsWzK12CAX765vLqx1CwXbu2lPtEAr23GvD530dTrGu3723\n2iEUbO6CBdUOYVg7XPuHShlO5/ZKeWf3zmqHUHOeW67zVHfD6fvbcFWWpMIYcxVwL/AtYCKwFHjO\nGHN0OdqrpD//efj8Uf519Ypqh1CwncPoC+Xzrw+f93U4xbp+z/D5G5j75z9XO4Rh63DuHyplOJ3b\nK+WdPbuqHULNeX6FzlPdDafvb8NVuUYqZgEPW2t/aa19E7gRSADXlak9EREZHtQ/iIgchkqeVBhj\nvMAk4E8HtllrLfAC8OFStyciIsOD+gcRkcNXOUYqjgbqgR2HbN8BHF+G9kREZHhQ/yAicpjyVDsA\nIACwatWqsjby5ptvsm5dlM2b1xZ8zL59O7B2L17vX/H7gwB0dOxheT+Tn2Kx3ezdu5U1a/7Gjh1N\nBbUz2GNisV3sie0lFtvCts5X8Xq9ecvt2Lub/32l64JgOp2iI7abdbs6D5a31mJSBgBvNEB9fX2P\n4xNOJ7FUB+uWvE4wGO43pkR8H3u2b6V+pY89u7ewjoGPOaAjtptMOs3KjW/jratnd/seOjesO/ie\n55NKOezavYdO1uL3B3rtTzpOr3pi8X1E93WydN0m3tkdKyi2RDzBhj3tZNdsIBQKARDd18nC5fn/\njtrjCbbvaWfJmg1EIqF+6xmKA/Xs3bAlbzuH6ivWQuPp6/UMtp6B7Gnfh5vJ8vpbb7F5374h19Of\n3bEYW/fu5W9r1tC049DvtIOzp6ODBcvzT5YtZTv9cVIp3kqlGLVkCQ0NDb32dzun9v6ADC8BgNfm\nv8bGtRuLqii2J0YmnuHNJW/iD/h77d+zYw+dG7cR3bR9wLqcRJItm3aw7W/LSSYddu/OUu/J3491\nplLsdBxes2m8h5xnAdxkiuiubex5E7ze3nEdam9HlFWbNhHZV8eWXXt4beU6IuF3z5dOp8M7O/aQ\nWr6OYLjv82hfYp3OwXrr4WBdHq+HzZv34PU51Nfn73f6s7t9Lx0ph9VbNrI93jFg+f7O893P8W4O\n3IzLqg3rCAYH/3q71xfd5CHW0dWmm7Psju0ZdL35+p/u9jlOj3r7e52ZTJpcRwaHxXi9/X+U2xMd\nbG/fyZINy4iERhHtbGfh2tf/f/buPT6q6t7//+tDJExIMM6pRfBbT0Xt8YaEJCKIJUqxgP7EiKVK\nLN5tvdRvOdRWpLVq1dp6jVq1x7bngNZj+sVLjXoEtZZLvaBHAygX6w2QKuBtGC6ZMMmwfn/sSZyE\nSTLJXJO8n4/HPHT2rL3XZ68Me81nr73Xjlu2vn4b67duIbL+LQYOLOrSdrurbZ27GnexoSlM49oX\n8fk6jqG7Yn9fdfb7LdX1JfobMFm7doXYtet9VqzYO25f0J509BHmjTynTnR4ux74jnPuyZjl84Bi\n59zUNuXPAv47pUGIiEiz7znnHs52END1/iH6mfoIEZH0SVkfkfKRCudco5m9AUwAngQwM4u+vzvO\nKs8C3wPWAw2pjkdEpI/yAQfiHWNzQjf6B1AfISKSDinvI1I+UgFgZmcA8/Bm9XgNb7aPacBhzjnN\n+yYi0kepfxAR6Z3Sck+Fc25+dM7x64H9gBXAJHUYIiJ9m/oHEZHeKS0jFSIiIiIi0nek6+F3IiIi\nIlk109gAACAASURBVCLSRyipEBERERGRpGQ0qTCzq8xst5nd0Um5E8zsDTNrMLN3zOzcTMUYE0On\nsZrZ8dEysa+ImQ1Oc2zXxql3TSfrZKVNuxprtto0pv79zexPZvaZmdWb2UozK+tknWy1bZdizeL3\ndV2ceneb2W87WCdbbdqlWLPYpv3M7AYz+yD6t3/PzK5OYL2sH1vbxDPHzF4zs21mtsXM/mJm/5bA\nejm1H6nSnfbI9jEz3czskuixLRh9vWxmkztZp1d+P5p1tU16+3ekLetBvzUzJZE2ScX3JGNJhZmN\nAn4ArOyk3IHA08ALQAlwF/BHM/t2mkOMjSGhWKMc8A28p8EOAYY65z5JY3jNVuHd5Nhc7zfbK5gD\nbZpwrFFZaVMz2wd4CdgFTAIOB64AAh2scyBZaNvuxBqVjbY9Oqa+IcC3o3HMj1c4y9/XLsUalY02\nvQq4GLgMOAy4ErjSzC5vb4UcOA7EMw74LTAaOBHoDzxnZu0+aSxH9yNVutweUdnqhzJhIzAbKAPK\ngb8BtWZ2eLzCvfz70axLbRLVm78jLXrSb81MyehvWudc2l9AEfAP4FvAIuCODsreDLzZZlkN8EwO\nxno8EAH2zkRsMfVeC9R1oXzW2rQbsWalTaN1/wZY0sV1stK23Yw1a23bJo47gXdyrU27GWu2jgFP\nAX9os+xR4MGe0K4dxLgvsBv4Zk/ejwy3R078u85wu3wOnN/Xvx9daJM+8R3pSb81c7RNkv6eZGqk\n4l7gKefc3xIoOwb4a5tlzwLHpjyq+LoSK4ABK8zsYzN7zszGpjG2WN8ws4/M7H0ze8jMDuigbLbb\ntCuxQvbadArwupnNj156UGdmF3WyTrbatjuxQvba1qvce6Ly94D/7KBYtr+vQMKxQnba9GVggpl9\nA8DMSoDjgGc6WCcn2rUT++CdKfuigzI9YT9SJZH2gCz/u84U8y77mw4MBF5pp1hf+n4k2ibQN74j\nPem3ZqZk9DdtWp5TESv6ZR+Jd1lBIoYAW9os2wLsbWYDnHO7UhlfrG7EugnvEoTXgQHA94HFZnaM\nc25FeqIEYBlwHl72ORS4DlhqZsOdczvjlM9am3Yj1my1KcBBwKXA7cCvgGOAu81sl3PuT+2sk622\n7U6s2WzbZlOBYuCBDspk8/saK5FYs9WmvwH2Bt42swjepaw/d879uYN1cqVd4zIzwxsZetE519E9\nYjm9H6nShfbIhX/XaWVmw/F+MPuA7cBU59zb7RTvK9+PrrRJX/iO9JjfmpmSjd+0aU0qzOxreAfF\nE51zjemsK1ndidU59w7wTsyiZWZ2MN4TYtN2w49zLvaR6qvM7DVgA3AGMDdd9XZHV2PNVptG9QNe\nc879Ivp+ZfTAfQnQ3g/1bOlyrFlu22YXAAucc5szVF8yOo01i216JnAWMB1Yg9dx3GVmH3eQVOa6\n+4Aj8EZcJMH2yJF/1+n2Nt5178V4T19/0MwqOvgR3Rck3Ca9/TvSk35rZkq2ftOm+/KncuCrQJ2Z\nNZpZI941WzPNLBw9E9PWZrybemPtB2xLc+bYnVjjeQ04JF1BxuOcC+J9EdqrN1ttuocEYo0nU226\nCVjbZtla4F87WCdbbdudWOPJ2PfVzP4V7+bTP3RSNOvf1y7EGk8m2vQW4DfOuUecc6udc/8NVANz\nOlgn6+3aHjO7BzgZOME5t6mT4jm7H6nSxfaIJ+P9UDo555qccx8455Y7536Od8PpzHaK9/rvB3S5\nTeLpTd+RnvRbM1Oy8ps23Zc//RU4qs2yeXg/fn7joneGtPEKcFKbZRPp+FrBVOhOrPGMxPvBlzFm\nVoT3R3+wnSLZatM9JBBrPJlq05eAQ9ssOxRvZKU92Wrb7sQaTya/rxfgDS93dN0/5Mb3NdFY48lE\nmw7Eu6Eu1m46PlGUC+26h+gP6ErgeOfchwmskpP7kSrdaI94Mt4PZVg/vMsz4unV348OdNQm8fSm\n70hP+q2ZKdn5TZuJu8/b3F3e6u5z4CbggZj3B+JdH3gz3o+ky4Aw3hBOrsU6EzgVOBg4Em+oqRHv\n7FI647oVqAC+DowFnsf7AfSVXGvTbsSalTaN1n003hStc6L1nxVtt+m59n3tZqzZbFsD1gO/ivNZ\nTrRpN2PN1jFgLvAh3tnsr+Pd//EJcFOutms7+3Ef3jTI4/DOEja/fD1pP7LcHln7d52hNrkp2h5f\nB4YDvwaagG9FP/91X/l+JNEmvfo70k4b9ZjfmjnUJkl/T9J+o3YcbbOjoUDLbEDOufVm9v/hDeX/\nCPgncKFzru1d+pnQYaxAPt6NsvsD9cCbwATn3NI0x/U14GHgK8CnwIvAGOfc5/HizHKbdilWstem\nOOdeN7OpeDfB/gJYB8x0rW9+zYm27U6sZLFt8S4lOoD49/zkRJvGSDhWstemlwM34M3sMRj4GPhd\ndFncWHOgXeO5BO84u7jN8vP5cjSzJ+xHqnS5Pcjuv+tMGIw3WcJQIIi3fxPdl7PZDKHvfD+adalN\n6P3fkXh60m/NTEn7b1qLZiciIiIiIiLdkrEnaouIiIiISO+kpEJERERERJKipEJERERERJKipEJE\nRERERJKipEJERERERJKipEJERERERJKipEJERERERJKipEJERERERJKipEJERERERJKipEJ6BTOb\na2aPd/D5uWYWyGRMHTGzdWb2oy6us9jMdptZxMxGpCu2aF27o68v0lmPiEgmddZXdGN7u83s1A4+\n/3q0TIfHbDNbZGZ3dLHuuTF9QrsxpEK0z2ruF/ZOZ13ScympkL7EZbrCFCczDvg9MARYlaJttmcI\n8O9prkNEpKcbAizopExL32Nmx6f4h/mCBGNI1tHAd8hCPyo9x17ZDkCklzNSexCud859msLtxeWc\n+8TMgumuR0SkJ3POfZJAMWvz/67NsmTsylCf8LlGrqUzGqmQpJnZNDN708zqzewzM3vOzApiPr/I\nzNaYWSj630tjPmseGj7TzF6KlnnLzCpiyvQzsz+a2QfROt7u6qVD7cRdaWZvROt8z8yuMbO8mM93\nm9mFZva4me00s3fMbEqbbZwaXV4f3e+zm89CmdnxwH8BxTFD1NfErF5oZv9pZtvMbIOZfb+b+3GE\nmT1lZsHotpaY2bDoZ3PN7C9mNsfMNptZwMyuNrM8M7vFzD43s41mdl536hYRSVRP6CvM7BMzOz3m\n/Qoz+yjm/TfNrMHMfNH3rS5/MrNjzKwuGt9rQCnRE0tm9nXgb9GigWif8F8x1fczs5ujx+VNZnZt\nV2KPieH/mFlNdDs7zOw1MxsV/exaM1tuZudH+53tZnZPtO2ujNa7xcx+1p26pW9TUiFJMbMhwMPA\nH4HDgOOBx4mehTGz7wHXAXOin/8MuN7Mzm6zqVuAW4GRwCvAk2bmj37WD9iIN/R6OPBL4FdmNi2J\nuMcBDwDV0bguBs6NxhfrGuDPwFHAM8B/m9k+0W0MAx6J7m9JtA1u4suRiZfxLiHaBuwHDAVui9n2\nj4H/je7zfcDvzOwbXdyP/YGlQAg4Aa8D+wOtRyG/Fa17HDALuB54GvgCOAb4D+D+6LZERFKuB/UV\nS/GOpUSP9YcBBWb2b9HPK4DXnHMNcfaxEHgK7/LUsuj+xB7zP4zGBvANvOPyzJjPzwV24B2XrwSu\nMbMJXYi9OYal0W2fgtd3/ZrWv/cOBiYDk4DpwEXA/wD7R/dvNnBjcyIikjDnnF56dfuF9yM2AhzQ\nzufvAme2WfZz4KXo/38d2A38JObzPLyD7086qPe3wPyY93OBxzsofy7wRcz754HZbcp8D/go5v1u\n4LqY9wOjyyZG3/8GWNlmGzdE22PvePXGlFsHzGuzbDPwgw72YRFwR5tlNwHvAXntrDMX+KDNsrXA\n4pj3/YDtwBkdtZleeumlV3dfPaivuBx4M/r/p+KdHHq8+dgMPAfcEFN+N3Bq9P9/AHwC5Md8fnF0\nv0dE3x8f20fElFsELGmz7FXgpg5i3WNfojFsBYrbWefa6PF+YMyyBcD7bcqtBa5ssyxu7Hrp1fzS\nPRWSrJXAC8AqM3sW74D7qHNuq5kNxDsj8p9m9seYdfLwDnqxljX/j3MuYmav451pAsDMfgicD/wr\nUADkA8uTiLsEGGtmV7eJK9/MfO7Ls1BvxcRVb2bbgMHRRf+GN9IQ67UuxPBWm/ebY7adqBLg7865\nSAdlVrd5v4XW+7XbzD7vRt0iIonqKX3FEuBOM/sK3o/oxXjH5hOilyqNBW5uZ93D8BKScMyyV7pQ\n95tt3m+ie33CcudcR/fErXfO1ce83wI0tSmzpRt1Sx+npEKS4pzbDUw0s2OBicD/xRtuPgbvkhzw\nhlbb/tju6EdwK2Y2HW+4exZeh7Idb2j4mCRCL8K7tGmPqQVd62HtxrYfk7rLBlOx7VDnReLWk879\nEhFppaf0Fc65t8y7IfkEvKTiZ3g/sK8CRuH9bno50e11kfoE6dH0hZGUcM694pz7Jd4QdxiY6rxZ\nMT4GDnbOfdDmtaHNJsY0/495N0uXA2uii8biDYHf75xb6Zz7AO+sVjLqgEPjxPVBF7bxD7xp9mK1\n7bzCeGfb0uVNYJzF3GAuIpKrekhf8SJQCRwR/f83gQF4lzK97pxr74f7WmCEmeXHLDu2TZnmUYx0\nHbPfBEY23/snkklKKiQp0Zku5phZuZkdgHcT2r58eZC/FphjZv/XzL5hZsPN7Dwza/sMhB+a2Wlm\ndijeTcv74F0vCt61tkeb2cToNq7HO2OUjOuBc8yb8ekIMzssOqvIDV3Yxv3AYWb2m2hcZ+DdhwBf\n3qy9Higys2+Z2VcsZqaTFLkH2Bv4f9G/wSFmNqOrN3yLiKRTD+srFgNVwArnXL1zzuHd/Pw9vMuj\n2vMw3rH/j2Z2uJmdDFzRpsyGaJkpZrZv9MbqVKrBG1l5wszGmtkwMzvdzEanuB6RPSipkGRtw5st\n4n/wztxfD/zYOfccgHPuP/GGtM/HO4OyGO+H97o227kq+lqBd7ZpinOueU7s+/EuU/oz3pD2vwD3\nJhN0NL5TgG/jDbe/gjdT0/rYYvFWjdnGemAaMBXveuGLgV9FP94VLfMK3uxK/w/vBr6fJrLtLuzH\nF3izOxXite3reO3ddii7s3r0QCMRSaee1Fcswft9tChm2eLossVtysb2CTuBKcBwvNHwG/AuvyKm\nzMd4CdRv8O7V+G034muXc64Rr1/7BK+t38SbzSnhy8iaN5XKuKRvMC8BF8mO6LzdHwClzrm2N6n1\nOGb2c7xZQr6ehm0vwrsB78ep3nY79Z2HN9vUv2SiPhGR9vS2viIVzGwu3ixPp3daODX1nYB3s73f\nObctE3VKz6KRCskFqXqyaMaZ2aVmdnR0iPls4CfAvDRWeZl5D7g7Mo11YGbb8S4t0FkHEckVPbav\nSKNTon3CyemsxMxW4T2rSX2CtEuzP0ku6MkHqW8AVwN+vPnSb8Ub1k6Hs/CmSCRaVzqVRP/b1SFz\nEZF06cl9RTr8FO8SK/Cmn02nk4D+ABqlkPbo8icREREREUmKLn8SEREREZGkKKkQEREREZGkKKkQ\nEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQERER\nEZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGk\ndDmpMLNxZvakmX1kZrvN7NQOyv5HtMyPkgtTRERynZldYmYrzSwYfb1sZpPblLnezD42s3oze97M\nDslWvCIikjrdGakoBFYAlwGuvUJmNhUYDXzUvdBERKSH2QjMBsqAcuBvQK2ZHQ5gZrOBy4EfAMcA\nO4FnzSw/O+GKiEiqmHPt5gWdr2y2GzjNOfdkm+X/B3gFmAQ8A1Q75+5OJlAREel5zOxz4CfOublm\n9jFwq3OuOvrZ3sAW4Fzn3PxsxikiIslJ+T0VZmbAg8Atzrm1qd6+iIjkPjPrZ2bTgYHAy2Y2DBgC\nvNBcxjm3DXgVODY7UYqISKrslYZtXgWEnXP3JFLYzL6CN6KxHmhIQzwiIn2RDzgQeNY593mmKjWz\n4Xgj1T5gOzDVOfcPMzsW75LZLW1W2YKXbLS3PfURIiKpl/I+IqVJhZmVAz8CSruw2iTgv1MZh4iI\ntPge8HAG63sbKAGKgWnAg2ZWkcT21EeIiKRPyvqIVI9UfBP4KrDRuwoKgDzgDjP7d+fcQXHWWQ/w\n0EMPcfjhh6c4nMyZNWsW1dXVKd3m9u3bWbhwOQMGHMaAAQUA7NoVYteut5k8uZRBgwaltL507EOm\naR9yg/Yh+9auXcuMGTMgeozNFOdcE/BB9O1yMzsGmAncAhiwH61HK/YDlnewyfXQ8/uIzvT071si\ntI+9R1/Yz96+j+noI1KdVDwIPN9m2XPR5XPbWacB4PDDD6esrCzF4WROcXFxyuMPBoOsWbMNv/9Y\nCguLAdi5M0gg0MjIkSMpLi5OaX3p2IdM0z7kBu1DTsn2JUP9gAHOuXVmthmYALwJLTdqjwbu7WD9\nXtFHdKYXfd/apX3sPfrCfvaFfYxKWR/R5aTCzAqBQ/DOOAEcZGYlwBfOuY1AoE35RmCzc+7dZIMV\nEZHcZWY3AQuAD4FBeMPqxwMTo0XuBK42s/fwzo7dAPwTqM14sCIiklLdGak4GliEd8OdA26PLn8A\nuCBO+e7PWSsiIj3JYLy+YCgQxBuRmOic+xuAc+4WMxsI3A/sA/wdOMk5F85SvCIikiJdTiqcc0vo\nwlS07dxHISI5IhgMsn79eiKRSMq2uXXrVurq6lK2vWzoKfuQl5fHgQcemPLLIbvDOXdRAmWuA65L\nezAikrT2+oeecnxMRm/Yx0z3D+mYUrZPqqqqynYISdM+5IZM7sNzzz1HZWUlDQ2pv+y+vLw85dvM\ntJ6yDz6fj9raWiZOnNh5YclJveHY1RntY8/SWf/QU46PyegN+5jJ/kFJRYr0hgOJ9iE3ZGofgsEg\nlZWVjB8/nmuuuYb8/PyM1CupFQ6Huf7666msrGTz5s05MWIhXdcbjl2d0T72HOofeodM9w9KKkT6\nqPXr19PQ0MA111zDmDFjsh2OJOGaa65hwYIFrF+/npKSkmyHIyI9nPqH3iOT/YOSCpE+qvka2dgz\nUKFQiHA49ffM5ufnU1BQkPLtiqf5b5jK+2JEpO+K1z+A+oieKJP9g5IKEQG8zqK2dhGBQOoPPH5/\nHpWV49VpiIj0UOojpDNKKkQE8K69DAQiFBSU4fMVpWy7DQ0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XXnghI0eOZOnSpS2xPfnk\nky3bueSSSzjxxBM59NBDmTZtGk1NTS3bnTVrVkv9N998MyNGjGDkyJGMHTuWhoYGtmzZwre+9S1G\njRrFUUcdxY9+9KOE20pEJBvUR6iPaI9GKkQkJ7377ru8+OKL9OvXjw0bNrR0JgsXLiQYDLJq1SoA\ntm7dGnf9888/n7lz53L66acDMG/ePC688MK4ZZctW8aKFSvYd999aWxsZPTo0Tz00ENUVFSwePFi\n5s2b126cK1euZPHixeTn5zNu3Dgee+wxzjzzTODLDvCBBx7gL3/5Cy+//DJFRUUEg0EGDBiA3+/n\n6aefZuDAgezevZvKykrmz5/PGWec0a02ExHpK9RH5B6NVIhITpoxYwb9+u15iCopKWHt2rVcfvnl\nzJ8/n732in9uZOrUqbz66qts2bKFnTt38vTTT3PWWWfFLXvyySez7777AvD222/Tv39/KioqADjh\nhBM46KCD2o1z6tSpDBgwADPjmGOO4f3339+jzP/8z/9wySWXUFRUBEBxcTFmRiQS4corr2TkyJGU\nlpbyxhtvsGLFio4bRkRE1EfkICUVIpKTmg+ubQ0bNow1a9YwefJkXnrpJYYPH04wGGTmzJmUlpZS\nVlbG6tWr8fl8fPe73+XBBx/kkUceYcKECa3m6E6krmYdXT/rixm6z8vLaxnaTsQdd9zBp59+yv/+\n7/+ycuVKqqqqaGhoSHh9EZG+Sn1E7tHlTyLSyo4UH7C6sr3mm+A6+uyjjz7C7/dzyimnMGnSJGpr\na9m4cSN33XXXHuucd955nHvuuQwePJg5c+YkFMOhhx5KY2MjS5cupaKigiVLlsQ9s9QVp556Kvfc\ncw+nn346e++9N8FgkEGDBhEIBBgyZAj9+/dn8+bNPPLII0ybNi2pukRE0kl9hPqI9iipyDGxc0AH\ng0EaG1M/H7RIPPn5+eT5/dQFAhAKpXTbeX4/+fn5nZZrPtsT76xP87K33nqr5eDf1NTEOeecw/Dh\nw+Nub9SoUeTl5fHBBx8wceLEPbYVT35+Pn/+85/54Q9/yO7duykvL+fQQw9tOYMVu25H24k1Y8YM\nNm3axNixY9lrr70oKirir3/9KzNnzmTatGkcddRR7L///nz7299OaHsiIpmmPsKjPqJ91lHWl5EA\nzMqAN954440+/7TUUChEbe0iAoFI9P1OVq9eR0XFhfzLvwwGYOfOIIHAUs46q4Li4uJshis9XPPT\nNGP/7enBRp4dO3a0DHe//vrrVFZW8v7777caxs4l8f6WMU9LLXfO1WU1wCSks48IBoM8/PBS/P4K\nCgu946mOsSLxjymgPqJZT+oj2vtbpqOP0EhFDgmHwwQCEQoKyvD5inDuY+rr3yMSSfz6O5FkFBQU\n9KgDe7o89thjVFdX45yjf//+PPTQQznZWYiIZJL6CI/6iPiUVOQgn6+IwsJi6uuD2Q5FpE8699xz\nOffcc7MdhoiI5CD1EfFp9icREREREUmKkgqRPiovLw8gLdfHSmY1/w2b/6YiIslQ/9B7ZLJ/0OVP\nIn3UgQceiM/n4/rrr+eaa65JaOYNyT3hcJjrr78en8/HgQcemO1wRKQXUP/QO2S6f1BSIdJHFRcX\nU1tbS2VlJQsWLMh2OJIEn89HbW2tZisSkZRQ/9B7ZLJ/UFIh0odNnDiRzZs3s379eiKRSNLb2759\nO8sXLqR0770ZlMAMIdtDIZZv20bp5MkMGjQo6fr7ory8PA488EAlFCKSUqnuHyTzMt0/KKkQ6eOK\ni4spKSlJybaCwSDb1qxhpN9PcWFh5+V37mRbIMDIkSP1o1hEJMeksn+Q3k83aouIiIiISFK6nFSY\n2Tgze9LMPjKz3WZ2apwy15vZx2ZWb2bPm9khqQlXRERERERyTXdGKgqBFcBlgGv7oZnNBi4HfgAc\nA+wEnjUzTR0gIiIiItILdfmeCufcQmAhgJlZnCIzgRucc09Hy5wDbAFOA+Z3P1QREREREclFKb2n\nwsyGAUOAF5qXOee2Aa8Cx6ayLhERERERyQ2pvlF7CN4lUVvaLN8S/UxERERERHoZTSkr0oeEQiHC\n4XBCZfPz8ylI4FkTIiIiIqlOKjYDBuxH69GK/YDlHa04a9asPeapr6qqoqqqKsUhivRNoVCI2gW1\nBEKBhMr7C/xUnlSpxKIHqKmpoaamptWyYDCYpWhERKQvSmlS4ZxbZ2abgQnAmwBmtjcwGri3o3Wr\nq6spKytLZTgiEiMcDhMIBSg4oADfQF+HZRvqGwhsDBAOh5VU9ADxTsDU1dVRXl6epYhERKSv6XJS\nYWaFwCF4IxIAB5lZCfCFc24jcCdwtZm9B6wHbgD+CdSmJGIRSYpvoI/CQZ0/7TpEKAPRiIiISG/Q\nnZGKo4FFeDdkO+D26PIHgAucc7eY2UDgfmAf4O/ASc65xC7kFhERERGRHqU7z6lYQiezRjnnrgOu\n615IIiIiIiLSk6R6SlkREemDzGyOmb1mZtvMbIuZ/cXM/i1OuevN7GMzqzez583skGzEKyIiqaWk\nQkREUmEc8Fu8iTlOBPoDz5lZy53+ZjYbuBz4AXAMsBN41szyMx+uiIikkp5TISIiSXPOnRz73szO\nAz4ByoEXo4tnAjc4556OljkHb/rx04D5GQtWRERSTiMVIiKSDvvgTebxBYCZDQOGAC80F3DObQNe\nBY7NRoAiIpI6SipERCSlzMzwphd/0Tm3Jrp4CF6SsaVN8S3Rz0REpAfT5U8iIpJq9wFHAMdlOxAR\nEckMJRUiIpIyZnYPcDIwzjm3KeajzXgPTd2P1qMV+wHLO9vurFmzKC4ubrUs3pPERUSktZqaGmpq\nalotCwaDKa9HSYWIiKRENKGoBI53zn0Y+5lzbp2ZbQYmAG9Gy++NN1vUvZ1tu7q6mrKystQHLSLS\ny8U7AVNXV0d5eXlK61FSISIiSTOz+4Aq4FRgp5ntF/0o6JxriP7/ncDVZvYesB64AfgnUJvhcEVE\nJMWUVIiISCpcgncj9uI2y88HHgRwzt1iZgOB+/Fmh/o7cJJzLpzBOEVEJA2UVIhIjxIKhQiHE/8N\nmp+fT0FBQecFJSnOuYRmE3TOXQdcl9ZgREQk45RUiEiPEQqFWFRbSyQQSHidPL+f8ZWVSixERETS\nSEmFiPQY4XCYSCBAWUEBRT5fp+V3NDRQFwgQDoeVVIiIiKSRkgoR6XGKfD6KCwsTKxwKpTcYERER\n0RO1RUREREQkOUoqREREREQkKUoqREREREQkKUoqREREREQkKUoqREREREQkKUoqREREREQkKUoq\nREREREQkKUoqREREREQkKSlPKsysn5ndYGYfmFm9mb1nZlenuh4REREREckN6Xii9lXAxcA5wBrg\naGCemW11zt2ThvpERERERCSL0pFUHAvUOucWRt9/aGZnAcekoS4REREREcmydNxT8TIwwcy+AWBm\nJcBxwDNpqEtERERERLIsHSMVvwH2Bt42swhe4vJz59yf01CXiIiIiIhkWTqSijOBs4DpePdUjATu\nMrOPnXN/SkN9Ij1OKBQiHA53Wi4/P5+CgoIMRNR77QqHCQaDCZVVe4uIiHRPOpKKW4BfO+ceib5f\nbWYHAnOAdpOKWbNmUVxc3GpZVVUVVVVVaQhRJHtCoRC1C2oJhAKdlvUX+Kk8qVI/dLspFA6zZuVK\ndkciCbVhnt/P+Mqe1941NTXU1NS0WpZoIiUiIpIK6UgqBgKRNst208n9G9XV1ZSVlaUhHJHcEg6H\nCYQCFBxQgG+gr91yDfUNBDYGCIfDPe5Hbq4INzXRLxSi1OdjsN/fYdkdDQ3UBXpme8c7AVNXV0d5\neXmWIhIRkb4mHUnFU8DVZvZPYDVQBswC/piGukR6LN9AH4WDCjssEyKUoWh6t0Kfj+LCjtsagJDa\nW0REpDvSkVRcDtwA3AsMBj4GfhddJiIiIiIivUzKkwrn3E7gx9GXiIiIiIj0cul4ToWIiIiIiPQh\nSipERERERCQpSipERERERCQpSipERERERCQpSipERERERCQpSipERERERCQpSipERCQlzGycmT1p\nZh+Z2W4zO7XN53Ojy2Nfz2QrXhERSR0lFSIikiqFwArgMsC1U2YBsB8wJPqqykxoIiKSTul4oraI\niPRBzrmFwEIAM7N2iu1yzn2auahERCQTNFIhIiKZdIKZbTGzt83sPjP7l2wHJCIiydNIRZaFQiHC\n4TAAwWCQxsZwp+uEw7sIBoMt7/Pz8ykoKEhbjJI94XC41d+6Pen4DmSzbum1FgCPAeuAg4FfA8+Y\n2bHOufYulxIRkR5ASUUWhUIhamsXEQhEou93snr1OioqxlFYGH+dcDjEypVriER2t/yQ8/vzqKwc\nrx92vUy4IczKVSuJRCKd/m39BX4qT6pM2Xcgm3VL7+Wcmx/zdrWZvQW8D5wALOpo3VmzZlFcXNxq\nWVVVFVVVuiVDRKQjNTU11NTUtFqWyEnDrlJSkUXhcJhAIEJBQRk+XxHOfUx9/XtEIk3trtPUFCYU\n6ofPV4rfP5iGhh0EAnWEw2H9qOtlmpqaCDWF8B3gw/8Vf7vlGuobCGwMpPQ7kM26pe9wzq0zs8+A\nQ+gkqaiurqasrCwzgYmI9CLxTsDU1dVRXl6e0nqUVOQAn6+IwsJi6usTzxp9vkIKC72zdqFQuiKT\nXOAr8FE4qJ2hq6gQ6fkSZLNu6f3M7GvAV4BN2Y5FRESSo6RCRERSwswK8UYdmmd+OsjMSoAvoq9r\n8e6p2BwtdzPwDvBs5qMVEZFUUlIhIiKpcjTeZUwu+ro9uvwBvGdXjADOAfYBPsZLJq5xzjVmPlQR\nEUklJRUiIpISzrkldDxV+eRMxSIiIpml51SIiIiIiEhSlFSIiIiIiEhSlFSIiIiIiEhSlFSIiIiI\niEhSlFSIiIiIiEhS0pJUmNn+ZvYnM/vMzOrNbKWZ6VGoIiIiIiK9UMqnlDWzfYCXgBeAScBnwDeA\nQKrrEhERERGR7EvHcyquAj50zl0Us2xDGuoREREREZEckI7Ln6YAr5vZfDPbYmZ1ZnZRp2uJiIiI\niEiPlI6RioOAS4HbgV8BxwB3m9ku59yf0lCfiIiISCuhUIhwONxhmfz8fAoKCjIUkUjvlo6koh/w\nmnPuF9H3K81sOHAJoKRCJA3C4TDBYLDDMsFgkMZwY4YiEhHJnlAoRO2CWgKhjm/n9Bf4qTypUomF\nSAqkI6nYBKxts2wtcHpHK82aNYvi4uJWy6qqqqiqqkptdCK9TLghzMpVK4lEIh12jKH6EKv/sZqK\nQysopDCDEUq61dTUUFNT02pZZ0mmSG8WDocJhAIUHFCAb6AvbpmG+gYCGwOEw2ElFSIpkI6k4iXg\n0DbLDqWTm7Wrq6spK9OssyJd1dTURKgphO8AH/6v+Nst5z5x1K+qJ9IUyWB0kgnxTsDU1dVRXl6e\npYhEcoNvoI/CQe2fRAkRymA0Ir1bOpKKauAlM5sDzAdGAxcB309DXSIS5SvouPOs31GfwWhERESk\nL0n57E/OudeBqUAV8Bbwc2Cmc+7Pqa5LRERERESyLx0jFTjnngGeSce2RUREREQkt6TjORUiIiIi\nItKHKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGkKKkQEREREZGk\nKKkQEREREZGkpOXhdyIiIiKSPqFQiHA4nHD5/Px8CgoK0hiR9HVKKkRERER6kFAoxKLaWiKBQMLr\n5Pn9jK+sVGIhaaOkQkQ6tathF02NTXssr99RT6g+RDAYBCAYDBJubMx0eCmzKxxu2ZdE6MyfiGRD\nOBwmEghQVlBAkc/XafkdDQ3UBQKEw2EdsyRtlFSISId2NexizQuv0m/bjj0+CzeEqd9Sz7LtUFBQ\nwM5QiHWrVzOuogIKC7MQbfeFwmHWrFzJ7kgk4U5XZ/5aM7NxwE+BcmAocJpz7sk2Za4HLgL2AV4C\nLnXOvZfpWEV6gyKfj+JEj7WhUHqDkT5PSYWIdKipsYl+23YwckA+Awfkt/qsof8udu5wHLfPPhQO\nHMjHzvFefT1NkUiWou2+cFMT/UIhSn0+Bvv9nZbXmb+4CoEVwH8Cj7f90MxmA5cD5wDrgRuBZ83s\ncOdc4heHi4hIzlFSISIJGTggn6KC1sPsewEMaKR44EAKCwsJ1tdnJbZUKtSZv25zzi0EFgKYmcUp\nMhO4wTn3dLTMOcAW4DRgfqbiFBGR1NOUsiIiknZmNgwYArzQvMw5tw14FTg2W3GJiEhqKKkQEZFM\nGAI4vJGJWFuin4mISA+my59EREREkpDIMyM0W5z0dkoqREQkEzYDBuxH69GK/YDlna08a9YsiouL\nWy2rqqqiqqoqlTGKdFkoFKJ2QS2BUMfPjPAX+Kk8SbPFSebV1NRQU1PTallXpk9PlJIKERFJO+fc\nOjPbDEwA3gQws72B0cC9na1fXV1NWVlZeoMU6YZwOEwgFKDggAJ8A+M/M6KhvoHARs0WJ9kR7wRM\nXV0d5eXlKa1HSYWIiKSEmRUCh+CNSAAcZGYlwBfOuY3AncDVZvYe3pSyNwD/BGqzEK5ISvkG+igc\n1P7McSE0W5z0bkoqREQkVY4GFuHdkO2A26PLHwAucM7dYmYDgfvxHn73d+AkPaNCRKTnU1IhIiIp\n4ZxbQiezCjrnrgOuy0Q8IiKSOWmfUtbMrjKz3WZ2R7rrEhERERGRzEtrUmFmo4AfACvTWY+IiIiI\niGRP2pIKMysCHgIuAramqx4REREREcmudI5U3As85Zz7WxrrEBERERGRLEvLjdpmNh0YiTcTiIiI\niIiI9GIpTyrM7Gt4c5Gf6JxrTPX2ZU/h8K49noyYn5+vB+xIRjQ1NVFfXw9AfX09TY2NhOrr2blz\nZ6tye+21FwMGDNhj/V3hcMJP9gwGg4QbdVgRERHJNekYqSgHvgrUmVnzA5DygAozuxwY4JxzbVea\nNWsWxcXFrZbFewKgtBYOh1i5cg2RyO5WSYTfn0dl5XglFpJWTY1NrNuwjt27d5Ofn8+n27az7qP1\nLFv5Kv69i1qVLcovYnT56FaJRSgcZs3KleyORBL6ru4MhVi3ejXjKiqgsP2HTPU1NTU11NTUtFqW\naKImIiKSCulIKv4KHNVm2TxgLfCbeAkFQHV1NWVlZWkIp3dragoTCvXD5yvF7x8MQEPDDgKBOsLh\nsJIKSatIJEI4Eqb/vvkUDSpi58Dd5G/Ip3DoQIr2+TKpCO9qZMdnO2hqamqVVISbmugXClHq8zHY\n7++0vo+d4736epoikbTsT08V7wRMXV0d5eXlWYpIRET6mpQnFc65ncCa2GVmthP43Dm3NtX1icfn\nK6Sw8MuRnlAoi8FIn5Of3x9fgY8BoV3s1X8v8gcMwFfga1UmzK521y/0+ShOYOQhGL3MSkRERHJL\npp6oHXd0QkRERESkK0KhEOFwOOHyus80MzKSVDjnvpWJekRERESk9wqFQiyqrSUSCCS8Tp7fz/jK\nSiUWaZapkQoRERERkaSEw2EigQBlBQUU+Xydlt/R0EBdIKD7TDNASYWIiIiI9ChFCd6LB+hG0wxJ\n5xO1RURERESkD1BSISIiIiIiSVFSISIiIiIiSVFSISIiIiIiSdGN2iIiIjHC4V0Eg8E9lnd1rvt4\nc+n31fnyP/vsM/6+7O80RZo6LLf/4P0ZO2YsZpahyDrX2TMRgsEgjeHGDEYkkpuUVIiIiESFwyFW\nrlxDJLJ7jx//fn8elZXjE0oKQqEQtbWLCAQi3d5Gb/LZZ5/x7ufvUjy0uN0y9dvr2blxJ8eOPjZn\nkopQKETtgloCofafiRCqD7H6H6upOLSCQhKcjUikF1JSISIiEtXUFCYU6ofPV4rfP7hleUPDDgKB\nuoTnug+HwwQCEQoKyvD5irq1jd4mLy+P/b++f7uff7b5M9iSwYASEA6HCYQCFBxQgG9g/GciuE8c\n9avqiTRF4n4u0lcoqRAREWnD5yuksLD1WfXuTHXv8xW12o6my++ZfAN9FA6KPwpRv6M+w9GI5CYl\nFSKd6Ox62mZ99VppSZ9Ev3vN9B0UEZFsUVIh0oFErqdt5i/wU3lSpX7USUqEQiEW1dYSCXT+3WuW\n5/czvlLfQRERyTwlFSIdSOR6WoCG+gYCGwN99lppSb1wOEwkEKCsoIAiX/vfvWY7GhqoC+g7KCIi\n2aGkQiQBHV1P2yyELpaW1Cvy+SguTHBGGV2wTyAQYPfu3Xss9/v99OunRzOJdEVXL8GMRCLk5eV1\nqY5MXLa5KxyOO010R3Q5adcpqRARkV7ho48+YsGCOnbtar3cDI4//iCOPPLI7AQm0gN19RLMXeEw\n/3j/fQ475BDy+/dPuJ50X7YZCodZs3IluyORLtWhy0m7TkmFiIj0Cg0NDWzduhdf/3pFq+Xr1y+n\noaEhS1GJ9ExdvQTz4y++4O1PP2XEYYcx2O9PqI5MXLYZbmqiXyhEqc+XU3H1RkoqREQkI8zsWuDa\nNovfds4dkcI68PlaXy7Wr1/XLscQkS8leglmsN6bWrewK5dsQsYu28zVuHoTJRUiIpJJq4AJQPMj\nk5uyGIuIiKSIkgoREcmkJufcp9kOQkREUktTYYiISCZ9w8w+MrP3zewhMzsg2wGJiEjylFSIiEim\nLAPOAyYBlwDDgKVm1oULnUVEJBfp8icREckI59yzMW9XmdlrwAbgDGBudqKSniaRZycEg0Eaw40Z\niqhn6OqzGoLBIOFGtaEkLuVJhZnNAaYChwEh4GVgtnPunVTXJSIiPZdzLmhm7wCHdFZ21qxZFBcX\nt1pWVVVFVVVVusKTHBQKhahdUEsg1PGzE0L1IVb/YzUVh1ZQiAbCuvOshp2hEOtWr2ZcRQV0ZdYk\nyTk1NTXU1NS0WtbVhwEmIh0jFeOA3wKvR7f/a+A5MzvcOaf5uUREBAAzK8JLKB7srGx1dTVlZWXp\nD0pyWjgcJhAKUHBAAb6B7T87wX3iqF9VT6QpksHocld3ntXwsXO8V19PU0Rt2NPFOwFTV1dHeXl5\nSutJeVLhnDs59r2ZnQd8ApQDL6a6Pvn/27v3MLuqOs3j3zeVqjp1yY2AoCKgBA02ItfGGwmI0iMz\nktG2UUAFmX5m0J5GRx4vPNM20zw9bTuo7a1xHG1psYVpu8dusFvFpoFwCx1CLhiSUEASEkgICanU\n7Zy6nFNr/ti7wslJnapzrX1O5f08z36eOnuvvfdv1apaa6+z917LzKw5SLoZ+DnRI0+vBf4EGAPu\nmGo/s0KpzhRd84p/e54eTM9gNM2jnLkaJuadMCvVTLxTsRAIwP4ZOJeZmTWu44HbgcXAXqIvmt4W\nQng50ajMzKxqde1USBLwDeChEMKmep7LzMwaWwjBL0CYmc1S9b5TcQvwZuCddT5PUygcsaKvr4+x\nsalHsKjXudva2kp+WWu2qvUIIqMljqzhUUlmj3JHU/H/nZmZzVZ161RI+g5wCXB+CGH3dOln+8ge\nmUyGO++8j97eXN66IZ58chvLlp1f14EVJjv3okUtrFhx4RF7gVPrEURGh0fZsHEDuRJG1vCoJLND\nJaOptCxaxIUrVtT8/+6OlSu57b772D82xlF33UVra2tdRvYwMzMrpi6dirhDsQJYHkLYUco+s31k\nj9HRUXp7c3R0nEUq1Q1ACLtIp58hl8vO6LmHhwfp7V3L6OjoEdupqPUIItlslkw2Q+p1KRYtnnpk\nDY9KMjuUO5rK4PAwa3t76/J/d/ny5Vxyzjk80NvLsiuuYMGCBXUZ2cPMzKyYesxTcQsKrKthAAAY\nkElEQVRwOXApMCTp2HhTXwhhuNbnazapVDddXdEdmXR6Zr9JzD93xoP7ArUfQSTVMfXxKjmmNbZy\nRlPxP56Zmc1Wc+pwzGuB+cD9wK685bI6nMvMzMzMzBJWj3kq6tFRMTMzMzOzBuUOgJmZmZmZVcWd\nCjMzMzMzq4o7FWZmZmZmVpV6T35nZmZmVpKJSUTnzJn8O8+Znjw0l8uRHkrT2t9KGA+TphkaGGJ0\neJShgSE6Uh3MbZ076VDlpUyQeiROkFnuJKJ9fX2MjnkC2UbkToWZmZklbnRklPVPrCcbsmiOJk0z\n05OH/mb9enY/8hj9z3bSlmqfNM1g3wC5nu3suuff6J8/j9y8Lt78nrfR3vFK+lInSF3UsYgV76v9\nBJmNqpJJRIcyGbY9+STnL1tGXWcOtrK5U2FmZmaJy45lGcoOkTohRWdX56RpZnry0OFMhuPHxli6\nYN4hnYR8/QQ6U+2cvqCb1lQ76waHGBsbOyR9KROkDqeH6d1ZnwkyG1W5k4gC7AqBZ9JpsjlPINto\n3KkwMzOzhjHVpKRJTB7aMmcOnak2UkXuVGTb20jNnUtnexstbW0wPFL0WNNNkJrhyJwgs5xJRPvS\nnkC2UflFbTMzMzMzq4o7FWZmZmZmVhV3KszMzMzMrCp+p6IGstksmzZtIpvNHlzX0tLCSSeddPBz\nX18fY2OjCUR35MjlcmzatImxEoaaGx4eZnx8fAaiajz9vf307+ufMk3vvl72v7Sf53qeo6urq+hQ\nilaecoZO9LCJZmbWTNypqIHdu3dz773PkcstPrhubGwXnZ3raG2N1mUyQzz55DaWLTvfI6DVyZ49\ne7hv3X2Mto0iTT4cIUAIgeyBLCEXWMzioulmqz3P7OTVz79ER3tb0TShf5A3PL+HhWu3cKCjg9Ew\nDgvnzWCUs0+5Qyd62EQzM2sm7lTUyPg4LFnytoMT9mzY8Hf09weOP/4sUqluQthFOv0MuVx2miNZ\nNcbDOEvesoSWuS1F0+RyOTav3DyDUTWawNFdnSx57auKpujr7SO7czenvvYYVu3rh+J9NCtRuUMn\nethEMzNrJu5U1Fkq1U1X1wLS6dJnizSz2avUoRM9bKKZmTUTv6htZmZmZmZVcafCzMzMzMyq4k6F\nmZmZmZlVxZ0KMzMzMzOrijsVZmZmZmZWFY/+ZGZmlrBMJsPo6OETpOZyOVpaDh0iu62traS5Tiw5\no8OjDGno4Of0YJrRkVHSg2mGOocOS5/L5RjJjNB3oI+dO3dOO0nmgQMH6OvvZ6i9veiF3Ny5c2lv\nb68mG2ZlcafCzMwsQZlMhjvvvI/e3kPnJBkdHeHZZ59iyZKltLa+MlnlokUtrFhxoTsWDWpsLEvP\nA2voGHtlXqrBvkGyTz3H8/f8Gwfmdx+WfueO3RyzeCFbN25l96o1tLa2Fj1+Nptl1wsv0jo2Rstv\nvYkFXZ2Tputu6+a8s89zx8JmjDsVZmZmCRodHaW3N0dHRzRZ6oT9+3exd+8Wli49nUWLoskqh4cH\n6e1dy+joqDsVDSqXy9EyMMQZC+bR2R51BvvHx+lsb+P0eZ3ML+hU7O0b4KX9/bx58UK65sLS182n\nu7t7skMDMNg/yOqdW9nbAh2vStG98PC0oyNjDO4bJJvNulNhM6Zu71RI+gNJ2yRlJD0q6dx6nasR\nrF27MukQqnbHHXckHULV1j66NukQqrbyn5r/b+n+DT1Jh1C1O1Y2fzk0qiOtfSjFY4+tPDhZ6sSS\nSkWTJKZSXXnril9sNrp1q9YlHULd3bt288GfO9vb6O5I0d2RoivVTkfrXLpS7QfXTSwdbVHHI9XW\nSkfrXBbN7+booxYUXRYt6KatpYWWlhba2ttJdaQOW9rai9/pqIUjoX5c+dhjSYfQdOrSqZD0YeBr\nwI3AmcAG4G5JR9fjfI1g3boHkw6hau5UNIYH/7n5/5ZW/mYWdCoebP5yaERHYvtQijVrZv/f2xHR\nqVi3efpEs8CRUD8+uGZN0iE0nXrdqfhvwPdCCLeFELYA1wJp4Jo6nc/MzJqD2wczs1mo5p0KSa3A\n2cC/TqwLIQTgHuDttT6fmZk1B7cPZmazVz1e1D4aaAH2FKzfA7ypDudrGOl0H1LUTwshICUc0BFq\naGCIlrktRbeP58ZnMJrGNJbLMZgZLrp9aHiEzFiWoeHDh7g0q0Ld24cQxhkaOnQ4zlwuC7RNvoPN\nnBDVz8WMNmh9k83lGMyMkC2y/ZX6coTcuNsXO3I1wuhPKYDNm5v3OcTe3l6y2SGeeOJHB9eFkCGd\nzpHNrqK9vYO+vn3s37+Lnp7V7NmzCOCwddN9LmUfgJGRDENDW3jooTl0d3czODjI1q1P0dXVSnt7\nByMjGUZGnmX9+vnMmzfvYMx9fX2sXdu87yT09fWRHcmy8Z6N0yfOQiabYWT9CO2p4iNj9O3vY/9L\n++l5ooc9Cwuvg8pPV0ra/gP9bHxsY83PPZGube5c9r3cD7v3Fk07nM6wY98Bnu7ZTujsoGVkjMcH\nh2gvGOYwPZhm+8sHyPVsp7OzkwODaV58+QD9g2ke3fj0wXTZsSxjfaO0dG8mlUodXL+vr49d+/ez\nuqeHRXum/r3VO31h2pf7+1m5sfjfUiPFDpAZGeHZkRHmr1/PvHnz8uvU1FT7NYGS24i9e/cyMNDD\nhoKBAubMgZ6eYxkcHDxkfWHdCIfXsRMK69XpTHbsYscYHBwknR5ky5ZVB9MWi6VY/d3odu/ezdjw\nGBt+taFompHhEdK9aXrW99DeMXm9XMt6cWR4hKHnh3jooYeKlumWp59mTd8AT2x6lrmtk18yHawv\nt2zjxcE02/YdoG1klMf39x2sMwvrynwT9ebGbS+wt0iafOnBNDsPDNALrO/ZTnf34WkL69xy6xOY\nug6arH6s9TlqtU8l58iMjDCUybA+rk9no3q0EYruPNdOfHs7DfxuCOGuvPV/DSwIIXygIP0VwE9q\nGoSZmU24MoRwe9JBQPntQ7zNbYSZWf3UrI2o+Z2KEMKYpMeBi4C7ACQp/vytSXa5G7gS2A4Ufx7D\nzMzKkQJOIqpjG0IF7QO4jTAzq4eatxE1v1MBIOky4K+JRvVYTTTax4eApSGE4s9cmJnZrOb2wcxs\ndqrLOxUhhJ/GY47fBBwLrAd+xw2GmdmRze2DmdnsVJc7FWZmZmZmduSo1+R3ZmZmZmZ2hEi8UyHp\nDyRtk5SR9Kikc5OOqVSSzpd0l6QXJI1LujTpmMol6QZJqyX1S9oj6R8kvTHpuMoh6VpJGyT1xcsj\nkv5d0nFVQ9IX47+prycdS6kk3RjHnL9sSjquckl6jaQfS9onKR3/bZ2VdFyliuvTwnIYl/TtpGMr\nptx2QNIFkh6XNCypR9JVMxVrpcrJo6Tlk5RfTtKrZjLmclTSHjZbOZabx2Yrx0qvB5qwHMvOZxOW\nZdnXRbUox0Q7FZI+DHwNuBE4E9gA3B0/b9sMuoieB/4U0KzPkZ0PfBs4D3gP0Ar8WlLHlHs1lp3A\nF4CziGbrvRe4U9KpiUZVofhi4z8T/T80m41Ez8kfFy/vSjac8khaCDwMjAC/A5wKXA/0JhlXmc7h\nld//ccB7ieqnnyYZVDHltgOSTgL+iWhW7rcC3wR+IOm9MxFvJSps6wJwCq+U46tDCC/VO9YqlNUe\nNmM5Ulmb30zlWPb1QJOWY6XXPc1UlmVdF9WsHEMIiS3Ao8A38z4LeB74fJJxVZiXceDSpOOoQT6O\njvPyrqRjqTIfLwOfSDqOCuLuBp4C3g3cB3w96ZjKiP1GYG3ScVSZhz8HViYdR43z9A2gJ+k4poiv\nrHYA+ArwRMG6O4BfJJ2XGuZxOZAD5icde4X5nbY9bMZyrCCPzV6O014PNHs5lpHPpi7LOA9Fr4tq\nVY6J3alQNAnS2US9IgBClIt7gLcnFZexkKg3vj/pQCohaY6kjwCdwKqk46nAXwI/DyHcm3QgFTol\nfjTgWUl/I+l1SQdUpvcDayT9NL4tvlbS7ycdVKXievZK4K+SjmUyFbYDb4u357t7ivSJqqKtE7Be\n0i5Jv5b0jvpGOuOaqhyr0MzlWMr1wGwox1Kve5qyLEu8LqpJOSb5+NPRQAtQOGf6HqLbSjbDJIno\nW82HQghN9Sy8pNMkDRA9tnIL8IEQwpaEwypL/E9/BnBD0rFU6FHgaqLHhq4FXg88IKkryaDK9Abg\nk0R3iy4Gvgt8S9LHEo2qch8AFgA/SjqQIippB44rkn6+pPbahlcTleRxN/BfgN8FPkj0KMP9ks6o\nV5AJaLZyrETTlmMZ1wNNXY5l5LPpyrLM66KalGNd5qmwpnUL8GbgnUkHUoEtRM8BLiCaSOs2Scua\npWMh6Xiiiu09IYSxpOOpRAghf1bOjZJWA88BlwG3JhNV2eYAq0MIX4o/b5B0GlEn6cfJhVWxa4Bf\nhhBeTDoQK10IoQfoyVv1qKSTiSYKbOiXYO0VTV6OzXw9UI6S8tmkZTnj10VJ3qnYR/R82rEF648F\n3ADOMEnfAS4BLggh7E46nnKFELIhhK0hhHUhhP9O9CLkp5OOqwxnA8cAayWNSRojeobz05JG429T\nmkoIoY+oEl6SdCxl2A1sLli3GTghgViqIukEopcQv590LFOopB14sUj6/hDCSG3Dq4latXWraa7/\npek0WznWSsOXY5nXA01bjjW47mnosizzuqgm5ZhYpyL+NvZx4KKJdfGF00XAI0nFdSSK/7FWABeG\nEHYkHU+NzAEa/tZrnnuAtxA9/vTWeFkD/A3w1vgZ7KYiqZuowm2mTurDwJsK1r2J6I5Ls7mG6Pb1\nL5IOpJgK24FV+eljF9Og71DVsK07g+b6X5pOU5VjDTV0OVZwPdCU5Vij656GLstJTHVdVJtyTPhN\n9MuANPBxYCnwPaK3049J+i35EuPvIrr4O4No5IDPxJ9fl3RsZeThFqLhMs8n6pVOLKmkYysjD38W\nx38icBrwZSALvDvp2KrMV7ON/nQzsCwuh3cA/0J0Ubs46djKyMM5RM+f3gCcDFwBDAAfSTq2MvMh\nYDvwP5OOpYRYp2wH4v/nH+WlPykuk68Qdfg+BYwSPTqYeH5qlMdPA5fGf4O/RfRo5BjRN6qJ56dI\nHqdsD2dJOZabx6Yqx1KuB+L2ttnLsZJ8NltZTnldVK//x0bI+KeIGr8MUY/onKRjKiP25XHFkitY\nfph0bGXkYbL4c8DHk46tjDz8ANga/w29CPyaJu9QxPm6l+bqVNxBNExmBtgB3A68Pum4KsjHJcAT\nRBeBTwLXJB1TBXl4b/x/vCTpWEqMt2g7QPQ+zr0F6ZcRffufAZ4GPpZ0HmqZR+Bzcb6GgL1EI0ct\nSzoP0+RvyvZwNpRjuXlstnIs5XpglpRj2flswrKc8rqoXuWo+EBmZmZmZmYVSXRGbTMzMzMza37u\nVJiZmZmZWVXcqTAzMzMzs6q4U2FmZmZmZlVxp8LMzMzMzKriToWZmZmZmVXFnQozMzMzM6uKOxVm\nZmZmZlYVdyrMzMzMzKwq7lRY05O0XNK4pPlTpBmXdOlMxlWMpBslrStzn6viPOQkfb1escXnujU+\nV8P8zszMqlVKW1Hm8W6V9LNp0tw3XZ0d1++9ZZ57eV6bMGUM1YrbrIk24bp6nsuamzsV1jAqqVjz\nhJoGUyNTXJhXEm8fcBzwpeqimtZ18XnMzBpOA7UV1wFXl7ODpG1FLswriSsAbyw3hgrcTNQmPF/n\n81iTm5t0AGZ5RIN2DhpECCHsnYGTDAADkup9KjOzSjREWxHXlUnbG0Lor+cJQghpIC0pV8/zWPPz\nnQqrifgW77fj5YCkvZJuKkjTJumrkp6XNChplaTl8bblwA+BBXm3dP843vZRSY9J6pe0W9JPJB1T\nZbzHS/pbSb2SXpb0j5JOzNt+q6R/kHS9pF2S9kn6jqSWvDTHSfpnSWlJz0i6LP9bKEnbiBq+f4zz\ntLUgho/G6Q9IukNSVwX5aJP0FUk7JA1L6pH0iXjbxO3xiyWtjeO8R9Ixkt4naZOkvvj3mar0d2lm\nVqpGbisk3Szp53mfPzNRh+ate1rSNfHPhzz+JKlT0m2SBiS9IOmzhXkHTgT+YiL2gu0Xx/XygKRf\nSjq21NjzjiFJn4/jHJa0XdIN8bYT4/P+nqQH4jZhtaRTJJ0b/+4GJP1C0uJyz23mToXV0seBMeBc\notvCn5X0n/K2/yVwHnAZ8Bbg74BfSjoZeBj4DNAPHAu8GvhqvN9c4I+A04EVRJXyrZUGKWkucDfR\n40TvBN4BDAC/irdNuBB4A3BBnLerOfQ284+JbgkvAz4EfBLIb8DOJfpG7ao43bl525bEebkE+PfA\ncuCLFWTnx8CHgf8KLAV+HxgsSHMj8Cng7cAJwE+Jyucj8fkvBv6wgnObmVWiUduKlcA7pYO3aZcB\ne4naACS9lqhNuK/I/l8FzgfeT1SvXgCclbf9g0SPEH2JqE14dd62LuB64Mr4GCfk5ascfw58HvgT\n4FSi9uHFgjT/A7gJOBPIArfH+/0h8C6i9ukmzMoVQvDipeqFqJLdWLDuyxPriCrIMeC4gjT/Avxp\n/PNVwP4SznUOkAM648/L48/zp9hnHLg0/vmjwKaC7W3AEPCe+POtwFZAeWn+Frg9/nlpfMwz87af\nHK+7brLz5q27kagT05m37ivAI1PEf9jvBjglPv6FRfaZ+L1ckLfuC/G6E/PWfRf4xVS/My9evHip\nxdLIbQWwgOgi+6z48z6iC/RH4s9XAjvy0t8K/Cz+uQsYBj6Yt31R3K58PW/dtvw2Ii8/OeCkvHWf\nBHZNkbfD8gJ0AxngE0X2OTGu16/OW/fh+DjL89Z9obCNLBa7Fy/5i+9UWC09WvB5FXBK/K3PaUAL\n0BPfXh2QNED0TdDJUx1U0tmS7pL0nKR+4P540wkVxnl6HFd+HC8D7QWxPBlCyH9udzfwqvjnNwJj\nIYSDoziFEJ4FSn15cHuInlOd7NilOoOoAXxgmnS/yft5D5AOITxXsK7cc5uZVaoh24oQQh+wAbhA\n0luAEeD/AGdK6oxjWFlk95OBVmB13vF6gadKOTdRvbw973MlbcKpRF+Q3TtNusI2AWBjwTq3CVY2\nv6htM6Wb+Bsgom9K8hU+rnNQXJH/CvglcAXRregT43VtVcSyJj5e4dvI+S9CjxVsC9TukcFaHDtT\nwblCjc5tZlYPSbcV9xM9+joKrAwhHJC0meiRpOVU9khSKSarl8sdLaPSNmGydW4TrGzuVFgtnVfw\n+e3A0yGEoGhehhbg2BDCw0X2H43T5FsKHAXcEEJ4AUDSb1cZ51qiZ3X3hhCKNlLTeAqYK+nMibsV\nkpYQ3e7ON8bheaqV3xBV/MuZ/pspM7NG0chtxUrgGqK6+1d56y4neuT0/iL7PUvUGTqPeOhVSYuI\n7mrn7zNZ7LXyNNEjWBcRvcw+mcRHzbLZyz1Rq6UT4hE73ijpcqKXh78BEEJ4muhlsNskfUDSSZJ+\nW9IXJb0v3n870C3p3ZIWS+oAdhBVwtdJer2iOR/+aJJzl/ONzk+InpW9U9K74lgukPRNSa8p5QAh\nhKeAfwW+H4+acSbwPSDNoZX2duAiScdKWlhGjKXE8BxwG/BDSSvifCyX9Ht5yTwurJk1mkZuKx4A\n5gH/gVc6A/cTvU+xO4TwzGQ7hRCGgL8CbpZ0oaTTiN65KByGdTuwTNJraj3CUghhhOj9vP8l6WOS\n3iDpPMWjVcUmy7/bCasJdyqslm4DOoieKf028BchhB/kbb86TvNVYAvwM6IX6XYAhBBWAf+b6IXo\nl4DPhRD2xft9CHiS6KW56yc593TfvhzcHkLIED0buwP4f8Am4PtE71SUM973x4hG1VgZH+f7RLfn\nh/PSXA+8F9hJdIek1q4F/p5otJTNRM//duZt97dSZtZoGratCCEcILoL/FIIoSde/QDRhff90+Tr\nc8CDwF3Ar+OfHy9I88fASUR3Nl6a5nhlCyHcBHyNaPSnTcD/5dBRCSfLv9sJqwkd+h6qWWXi8bfX\nhRA+O23iWUrS8USN3kUhhGJDDlZ67KuIGt6janncac45DvzHEMJdM3VOM5vd3FbUhqL5Ou4Fjopf\nMJ+Jc24jaoe+NRPns+bjOxVmFYpvcb8/vj3/DqJvhLYy/WhMlVqgaFKnL9fp+ABI+m482oq/cTAz\na2w7Jf2knieQdEPcJryunuex5ucXta1WjsQL0Fbgz4DXE8078TBweQih8BnaWvh7olvpAAfqcPx8\nXwJujn/eXedzmdmR5UhsK+rhUaIXx2GKUbFq5LtEj5rBoSMkmh3Cjz+ZmZmZmVlV/PiTmZmZmZlV\nxZ0KMzMzMzOrijsVZmZmZmZWFXcqzMzMzMysKu5UmJmZmZlZVdypMDMzMzOzqrhTYWZmZmZmVXGn\nwszMzMzMquJOhZmZmZmZVeX/A+M5eoSb/i8nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x261bae95400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "\n",
    "label_dict = {1: 'Iris-Setosa',\n",
    "              2: 'Iris-Versicolor',\n",
    "              3: 'Iris-Virgnica'}\n",
    "\n",
    "feature_dict = {0: 'sepal length [cm]',\n",
    "                1: 'sepal width [cm]',\n",
    "                2: 'petal length [cm]',\n",
    "                3: 'petal width [cm]'}\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "for cnt in range(4):\n",
    "    plt.subplot(2, 2, cnt+1)\n",
    "    for lab in ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica'):\n",
    "        plt.hist(X[y==lab, cnt],\n",
    "                     label=lab,\n",
    "                     bins=10,\n",
    "                     alpha=0.3,)\n",
    "    plt.xlabel(feature_dict[cnt])\n",
    "    plt.legend(loc='upper right', fancybox=True, fontsize=8)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.1483555  -0.11805969 -1.35396443 -1.32506301]\n",
      " [-1.3905423   0.34485856 -1.41098555 -1.32506301]\n",
      " [-1.51163569  0.11339944 -1.29694332 -1.32506301]\n",
      " [-1.02726211  1.27069504 -1.35396443 -1.32506301]\n",
      " [-0.54288852  1.9650724  -1.18290109 -1.0614657 ]\n",
      " [-1.51163569  0.8077768  -1.35396443 -1.19326436]\n",
      " [-1.02726211  0.8077768  -1.29694332 -1.32506301]\n",
      " [-1.75382249 -0.34951881 -1.35396443 -1.32506301]\n",
      " [-1.1483555   0.11339944 -1.29694332 -1.45686167]\n",
      " [-0.54288852  1.50215416 -1.29694332 -1.32506301]\n",
      " [-1.2694489   0.8077768  -1.23992221 -1.32506301]\n",
      " [-1.2694489  -0.11805969 -1.35396443 -1.45686167]\n",
      " [-1.87491588 -0.11805969 -1.52502777 -1.45686167]\n",
      " [-0.05851493  2.19653152 -1.46800666 -1.32506301]\n",
      " [-0.17960833  3.122368   -1.29694332 -1.0614657 ]\n",
      " [-0.54288852  1.9650724  -1.41098555 -1.0614657 ]\n",
      " [-0.90616871  1.03923592 -1.35396443 -1.19326436]\n",
      " [-0.17960833  1.73361328 -1.18290109 -1.19326436]\n",
      " [-0.90616871  1.73361328 -1.29694332 -1.19326436]\n",
      " [-0.54288852  0.8077768  -1.18290109 -1.32506301]\n",
      " [-0.90616871  1.50215416 -1.29694332 -1.0614657 ]\n",
      " [-1.51163569  1.27069504 -1.58204889 -1.32506301]\n",
      " [-0.90616871  0.57631768 -1.18290109 -0.92966704]\n",
      " [-1.2694489   0.8077768  -1.06885886 -1.32506301]\n",
      " [-1.02726211 -0.11805969 -1.23992221 -1.32506301]\n",
      " [-1.02726211  0.8077768  -1.23992221 -1.0614657 ]\n",
      " [-0.78507531  1.03923592 -1.29694332 -1.32506301]\n",
      " [-0.78507531  0.8077768  -1.35396443 -1.32506301]\n",
      " [-1.3905423   0.34485856 -1.23992221 -1.32506301]\n",
      " [-1.2694489   0.11339944 -1.23992221 -1.32506301]\n",
      " [-0.54288852  0.8077768  -1.29694332 -1.0614657 ]\n",
      " [-0.78507531  2.42799064 -1.29694332 -1.45686167]\n",
      " [-0.42179512  2.65944976 -1.35396443 -1.32506301]\n",
      " [-1.1483555   0.11339944 -1.29694332 -1.45686167]\n",
      " [-1.02726211  0.34485856 -1.46800666 -1.32506301]\n",
      " [-0.42179512  1.03923592 -1.41098555 -1.32506301]\n",
      " [-1.1483555   0.11339944 -1.29694332 -1.45686167]\n",
      " [-1.75382249 -0.11805969 -1.41098555 -1.32506301]\n",
      " [-0.90616871  0.8077768  -1.29694332 -1.32506301]\n",
      " [-1.02726211  1.03923592 -1.41098555 -1.19326436]\n",
      " [-1.63272909 -1.73827353 -1.41098555 -1.19326436]\n",
      " [-1.75382249  0.34485856 -1.41098555 -1.32506301]\n",
      " [-1.02726211  1.03923592 -1.23992221 -0.79786838]\n",
      " [-0.90616871  1.73361328 -1.06885886 -1.0614657 ]\n",
      " [-1.2694489  -0.11805969 -1.35396443 -1.19326436]\n",
      " [-0.90616871  1.73361328 -1.23992221 -1.32506301]\n",
      " [-1.51163569  0.34485856 -1.35396443 -1.32506301]\n",
      " [-0.66398191  1.50215416 -1.29694332 -1.32506301]\n",
      " [-1.02726211  0.57631768 -1.35396443 -1.32506301]\n",
      " [ 1.39460583  0.34485856  0.52773232  0.25652088]\n",
      " [ 0.66804545  0.34485856  0.41369009  0.38831953]\n",
      " [ 1.27351244  0.11339944  0.64177455  0.38831953]\n",
      " [-0.42179512 -1.73827353  0.12858453  0.12472222]\n",
      " [ 0.78913885 -0.58097793  0.47071121  0.38831953]\n",
      " [-0.17960833 -0.58097793  0.41369009  0.12472222]\n",
      " [ 0.54695205  0.57631768  0.52773232  0.52011819]\n",
      " [-1.1483555  -1.50681441 -0.27056327 -0.27067375]\n",
      " [ 0.91023225 -0.34951881  0.47071121  0.12472222]\n",
      " [-0.78507531 -0.81243705  0.07156341  0.25652088]\n",
      " [-1.02726211 -2.43265089 -0.15652104 -0.27067375]\n",
      " [ 0.06257847 -0.11805969  0.24262675  0.38831953]\n",
      " [ 0.18367186 -1.96973265  0.12858453 -0.27067375]\n",
      " [ 0.30476526 -0.34951881  0.52773232  0.25652088]\n",
      " [-0.30070172 -0.34951881 -0.09949993  0.12472222]\n",
      " [ 1.03132564  0.11339944  0.35666898  0.25652088]\n",
      " [-0.30070172 -0.11805969  0.41369009  0.38831953]\n",
      " [-0.05851493 -0.81243705  0.18560564 -0.27067375]\n",
      " [ 0.42585866 -1.96973265  0.41369009  0.38831953]\n",
      " [-0.30070172 -1.27535529  0.07156341 -0.1388751 ]\n",
      " [ 0.06257847  0.34485856  0.58475344  0.78371551]\n",
      " [ 0.30476526 -0.58097793  0.12858453  0.12472222]\n",
      " [ 0.54695205 -1.27535529  0.64177455  0.38831953]\n",
      " [ 0.30476526 -0.58097793  0.52773232 -0.00707644]\n",
      " [ 0.66804545 -0.34951881  0.29964787  0.12472222]\n",
      " [ 0.91023225 -0.11805969  0.35666898  0.25652088]\n",
      " [ 1.15241904 -0.58097793  0.58475344  0.25652088]\n",
      " [ 1.03132564 -0.11805969  0.69879566  0.65191685]\n",
      " [ 0.18367186 -0.34951881  0.41369009  0.38831953]\n",
      " [-0.17960833 -1.04389617 -0.15652104 -0.27067375]\n",
      " [-0.42179512 -1.50681441  0.0145423  -0.1388751 ]\n",
      " [-0.42179512 -1.50681441 -0.04247882 -0.27067375]\n",
      " [-0.05851493 -0.81243705  0.07156341 -0.00707644]\n",
      " [ 0.18367186 -0.81243705  0.75581678  0.52011819]\n",
      " [-0.54288852 -0.11805969  0.41369009  0.38831953]\n",
      " [ 0.18367186  0.8077768   0.41369009  0.52011819]\n",
      " [ 1.03132564  0.11339944  0.52773232  0.38831953]\n",
      " [ 0.54695205 -1.73827353  0.35666898  0.12472222]\n",
      " [-0.30070172 -0.11805969  0.18560564  0.12472222]\n",
      " [-0.42179512 -1.27535529  0.12858453  0.12472222]\n",
      " [-0.42179512 -1.04389617  0.35666898 -0.00707644]\n",
      " [ 0.30476526 -0.11805969  0.47071121  0.25652088]\n",
      " [-0.05851493 -1.04389617  0.12858453 -0.00707644]\n",
      " [-1.02726211 -1.73827353 -0.27056327 -0.27067375]\n",
      " [-0.30070172 -0.81243705  0.24262675  0.12472222]\n",
      " [-0.17960833 -0.11805969  0.24262675 -0.00707644]\n",
      " [-0.17960833 -0.34951881  0.24262675  0.12472222]\n",
      " [ 0.42585866 -0.34951881  0.29964787  0.12472222]\n",
      " [-0.90616871 -1.27535529 -0.44162661 -0.1388751 ]\n",
      " [-0.17960833 -0.58097793  0.18560564  0.12472222]\n",
      " [ 0.54695205  0.57631768  1.2690068   1.70630611]\n",
      " [-0.05851493 -0.81243705  0.75581678  0.91551417]\n",
      " [ 1.51569923 -0.11805969  1.21198569  1.17911148]\n",
      " [ 0.54695205 -0.34951881  1.04092235  0.78371551]\n",
      " [ 0.78913885 -0.11805969  1.15496457  1.31091014]\n",
      " [ 2.12116622 -0.11805969  1.61113348  1.17911148]\n",
      " [-1.1483555  -1.27535529  0.41369009  0.65191685]\n",
      " [ 1.75788602 -0.34951881  1.44007014  0.78371551]\n",
      " [ 1.03132564 -1.27535529  1.15496457  0.78371551]\n",
      " [ 1.63679263  1.27069504  1.32602791  1.70630611]\n",
      " [ 0.78913885  0.34485856  0.75581678  1.04731282]\n",
      " [ 0.66804545 -0.81243705  0.869859    0.91551417]\n",
      " [ 1.15241904 -0.11805969  0.98390123  1.17911148]\n",
      " [-0.17960833 -1.27535529  0.69879566  1.04731282]\n",
      " [-0.05851493 -0.58097793  0.75581678  1.57450745]\n",
      " [ 0.66804545  0.34485856  0.869859    1.4427088 ]\n",
      " [ 0.78913885 -0.11805969  0.98390123  0.78371551]\n",
      " [ 2.24225961  1.73361328  1.6681546   1.31091014]\n",
      " [ 2.24225961 -1.04389617  1.78219682  1.4427088 ]\n",
      " [ 0.18367186 -1.96973265  0.69879566  0.38831953]\n",
      " [ 1.27351244  0.34485856  1.09794346  1.4427088 ]\n",
      " [-0.30070172 -0.58097793  0.64177455  1.04731282]\n",
      " [ 2.24225961 -0.58097793  1.6681546   1.04731282]\n",
      " [ 0.54695205 -0.81243705  0.64177455  0.78371551]\n",
      " [ 1.03132564  0.57631768  1.09794346  1.17911148]\n",
      " [ 1.63679263  0.34485856  1.2690068   0.78371551]\n",
      " [ 0.42585866 -0.58097793  0.58475344  0.78371551]\n",
      " [ 0.30476526 -0.11805969  0.64177455  0.78371551]\n",
      " [ 0.66804545 -0.58097793  1.04092235  1.17911148]\n",
      " [ 1.63679263 -0.11805969  1.15496457  0.52011819]\n",
      " [ 1.87897942 -0.58097793  1.32602791  0.91551417]\n",
      " [ 2.48444641  1.73361328  1.49709126  1.04731282]\n",
      " [ 0.66804545 -0.58097793  1.04092235  1.31091014]\n",
      " [ 0.54695205 -0.58097793  0.75581678  0.38831953]\n",
      " [ 0.30476526 -1.04389617  1.04092235  0.25652088]\n",
      " [ 2.24225961 -0.11805969  1.32602791  1.4427088 ]\n",
      " [ 0.54695205  0.8077768   1.04092235  1.57450745]\n",
      " [ 0.66804545  0.11339944  0.98390123  0.78371551]\n",
      " [ 0.18367186 -0.11805969  0.58475344  0.78371551]\n",
      " [ 1.27351244  0.11339944  0.92688012  1.17911148]\n",
      " [ 1.03132564  0.11339944  1.04092235  1.57450745]\n",
      " [ 1.27351244  0.11339944  0.75581678  1.4427088 ]\n",
      " [-0.05851493 -0.81243705  0.75581678  0.91551417]\n",
      " [ 1.15241904  0.34485856  1.21198569  1.4427088 ]\n",
      " [ 1.03132564  0.57631768  1.09794346  1.70630611]\n",
      " [ 1.03132564 -0.11805969  0.81283789  1.4427088 ]\n",
      " [ 0.54695205 -1.27535529  0.69879566  0.91551417]\n",
      " [ 0.78913885 -0.11805969  0.81283789  1.04731282]\n",
      " [ 0.42585866  0.8077768   0.92688012  1.4427088 ]\n",
      " [ 0.06257847 -0.11805969  0.75581678  0.78371551]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X_std = StandardScaler().fit_transform(X)\n",
    "print (X_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance matrix \n",
      "[[ 1.00675676 -0.10448539  0.87716999  0.82249094]\n",
      " [-0.10448539  1.00675676 -0.41802325 -0.35310295]\n",
      " [ 0.87716999 -0.41802325  1.00675676  0.96881642]\n",
      " [ 0.82249094 -0.35310295  0.96881642  1.00675676]]\n"
     ]
    }
   ],
   "source": [
    "mean_vec = np.mean(X_std, axis=0)\n",
    "cov_mat = (X_std - mean_vec).T.dot((X_std - mean_vec)) / (X_std.shape[0]-1)\n",
    "print('Covariance matrix \\n%s' %cov_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy covariance matrix: \n",
      "[[ 1.00675676 -0.10448539  0.87716999  0.82249094]\n",
      " [-0.10448539  1.00675676 -0.41802325 -0.35310295]\n",
      " [ 0.87716999 -0.41802325  1.00675676  0.96881642]\n",
      " [ 0.82249094 -0.35310295  0.96881642  1.00675676]]\n"
     ]
    }
   ],
   "source": [
    "print('NumPy covariance matrix: \\n%s' %np.cov(X_std.T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvectors \n",
      "[[ 0.52308496 -0.36956962 -0.72154279  0.26301409]\n",
      " [-0.25956935 -0.92681168  0.2411952  -0.12437342]\n",
      " [ 0.58184289 -0.01912775  0.13962963 -0.80099722]\n",
      " [ 0.56609604 -0.06381646  0.63380158  0.52321917]]\n",
      "\n",
      "Eigenvalues \n",
      "[ 2.92442837  0.93215233  0.14946373  0.02098259]\n"
     ]
    }
   ],
   "source": [
    "cov_mat = np.cov(X_std.T)\n",
    "\n",
    "eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n",
    "\n",
    "print('Eigenvectors \\n%s' %eig_vecs)\n",
    "print('\\nEigenvalues \\n%s' %eig_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2.9244283691111144, array([ 0.52308496, -0.25956935,  0.58184289,  0.56609604])), (0.93215233025350641, array([-0.36956962, -0.92681168, -0.01912775, -0.06381646])), (0.14946373489813314, array([-0.72154279,  0.2411952 ,  0.13962963,  0.63380158])), (0.020982592764270606, array([ 0.26301409, -0.12437342, -0.80099722,  0.52321917]))]\n",
      "----------\n",
      "Eigenvalues in descending order:\n",
      "2.92442836911\n",
      "0.932152330254\n",
      "0.149463734898\n",
      "0.0209825927643\n"
     ]
    }
   ],
   "source": [
    "# Make a list of (eigenvalue, eigenvector) tuples\n",
    "eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\n",
    "print (eig_pairs)\n",
    "print ('----------')\n",
    "# Sort the (eigenvalue, eigenvector) tuples from high to low\n",
    "eig_pairs.sort(key=lambda x: x[0], reverse=True)\n",
    "\n",
    "# Visually confirm that the list is correctly sorted by decreasing eigenvalues\n",
    "print('Eigenvalues in descending order:')\n",
    "for i in eig_pairs:\n",
    "    print(i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[72.620033326920336, 23.147406858644135, 3.7115155645845164, 0.52104424985101538]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  72.62003333,   95.76744019,   99.47895575,  100.        ])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot = sum(eig_vals)\n",
    "var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]\n",
    "print (var_exp)\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "cum_var_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n",
      "-----------\n",
      "[ 1  3  6 10]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4])\n",
    "print (a)\n",
    "print ('-----------')\n",
    "print (np.cumsum(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S06dP9xxPs2bNOP/881myZAlLly6lZs2atGvXLk0dr9dOTpQoUQLnXJrtnj59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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x261b963a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.bar(range(4), var_exp, alpha=0.5, align='center',\n",
    "            label='individual explained variance')\n",
    "plt.step(range(4), cum_var_exp, where='mid',\n",
    "             label='cumulative explained variance')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Principal components')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix W:\n",
      " [[ 0.52308496 -0.36956962]\n",
      " [-0.25956935 -0.92681168]\n",
      " [ 0.58184289 -0.01912775]\n",
      " [ 0.56609604 -0.06381646]]\n"
     ]
    }
   ],
   "source": [
    "matrix_w = np.hstack((eig_pairs[0][1].reshape(4,1),\n",
    "                      eig_pairs[1][1].reshape(4,1)))\n",
    "\n",
    "print('Matrix W:\\n', matrix_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.10795032,  0.64427554],\n",
       "       [-2.38797131,  0.30583307],\n",
       "       [-2.32487909,  0.56292316],\n",
       "       [-2.40508635, -0.687591  ],\n",
       "       [-2.08320351, -1.53025171],\n",
       "       [-2.4636848 , -0.08795413],\n",
       "       [-2.25174963, -0.25964365],\n",
       "       [-2.3645813 ,  1.08255676],\n",
       "       [-2.20946338,  0.43707676],\n",
       "       [-2.17862017, -1.08221046],\n",
       "       [-2.34525657, -0.17122946],\n",
       "       [-2.24590315,  0.6974389 ],\n",
       "       [-2.66214582,  0.92447316],\n",
       "       [-2.2050227 , -1.90150522],\n",
       "       [-2.25993023, -2.73492274],\n",
       "       [-2.21591283, -1.52588897],\n",
       "       [-2.20705382, -0.52623535],\n",
       "       [-1.9077081 , -1.4415791 ],\n",
       "       [-2.35411558, -1.17088308],\n",
       "       [-1.93202643, -0.44083479],\n",
       "       [-2.21942518, -0.96477499],\n",
       "       [-2.79116421, -0.50421849],\n",
       "       [-1.83814105, -0.11729122],\n",
       "       [-2.24572458, -0.17450151],\n",
       "       [-1.97825353,  0.59734172],\n",
       "       [-2.06935091, -0.27755619],\n",
       "       [-2.18514506, -0.56366755],\n",
       "       [-2.15824269, -0.34805785],\n",
       "       [-2.28843932,  0.30256102],\n",
       "       [-2.16501749,  0.47232759],\n",
       "       [-1.8491597 , -0.45547527],\n",
       "       [-2.62023392, -1.84237072],\n",
       "       [-2.44885384, -2.1984673 ],\n",
       "       [-2.20946338,  0.43707676],\n",
       "       [-2.23112223,  0.17266644],\n",
       "       [-2.06147331, -0.6957435 ],\n",
       "       [-2.20946338,  0.43707676],\n",
       "       [-2.45783833,  0.86912843],\n",
       "       [-2.1884075 , -0.30439609],\n",
       "       [-2.30357329, -0.48039222],\n",
       "       [-1.89932763,  2.31759817],\n",
       "       [-2.57799771,  0.4400904 ],\n",
       "       [-1.98020921, -0.50889705],\n",
       "       [-2.14679556, -1.18365675],\n",
       "       [-2.09668176,  0.68061705],\n",
       "       [-2.39554894, -1.16356284],\n",
       "       [-2.41813611,  0.34949483],\n",
       "       [-2.24196231, -1.03745802],\n",
       "       [-2.22484727, -0.04403395],\n",
       "       [ 1.09225538, -0.86148748],\n",
       "       [ 0.72045861, -0.59920238],\n",
       "       [ 1.2299583 , -0.61280832],\n",
       "       [ 0.37598859,  1.756516  ],\n",
       "       [ 1.05729685,  0.21303055],\n",
       "       [ 0.36816104,  0.58896262],\n",
       "       [ 0.73800214, -0.77956125],\n",
       "       [-0.52021731,  1.84337921],\n",
       "       [ 0.9113379 , -0.02941906],\n",
       "       [-0.01292322,  1.02537703],\n",
       "       [-0.15020174,  2.65452146],\n",
       "       [ 0.42437533,  0.05686991],\n",
       "       [ 0.52894687,  1.77250558],\n",
       "       [ 0.70241525,  0.18484154],\n",
       "       [-0.05385675,  0.42901221],\n",
       "       [ 0.86277668, -0.50943908],\n",
       "       [ 0.33388091,  0.18785518],\n",
       "       [ 0.13504146,  0.7883247 ],\n",
       "       [ 1.19457128,  1.63549265],\n",
       "       [ 0.13677262,  1.30063807],\n",
       "       [ 0.72711201, -0.40394501],\n",
       "       [ 0.45564294,  0.41540628],\n",
       "       [ 1.21038365,  0.94282042],\n",
       "       [ 0.61327355,  0.4161824 ],\n",
       "       [ 0.68512164,  0.06335788],\n",
       "       [ 0.85951424, -0.25016762],\n",
       "       [ 1.23906722,  0.08500278],\n",
       "       [ 1.34575245, -0.32669695],\n",
       "       [ 0.64732915,  0.22336443],\n",
       "       [-0.06728496,  1.05414028],\n",
       "       [ 0.10033285,  1.56100021],\n",
       "       [-0.00745518,  1.57050182],\n",
       "       [ 0.2179082 ,  0.77368423],\n",
       "       [ 1.04116321,  0.63744742],\n",
       "       [ 0.20719664,  0.27736006],\n",
       "       [ 0.42154138, -0.85764157],\n",
       "       [ 1.03691937, -0.52112206],\n",
       "       [ 1.015435  ,  1.39413373],\n",
       "       [ 0.0519502 ,  0.20903977],\n",
       "       [ 0.25582921,  1.32747797],\n",
       "       [ 0.25384813,  1.11700714],\n",
       "       [ 0.60915822, -0.02858679],\n",
       "       [ 0.31116522,  0.98711256],\n",
       "       [-0.39679548,  2.01314578],\n",
       "       [ 0.26536661,  0.85150613],\n",
       "       [ 0.07385897,  0.17160757],\n",
       "       [ 0.20854936,  0.37771566],\n",
       "       [ 0.55843737,  0.15286277],\n",
       "       [-0.47853403,  1.53421644],\n",
       "       [ 0.23545172,  0.59332536],\n",
       "       [ 1.8408037 , -0.86943848],\n",
       "       [ 1.13831104,  0.70171953],\n",
       "       [ 2.19615974, -0.54916658],\n",
       "       [ 1.42613827,  0.05187679],\n",
       "       [ 1.8575403 , -0.28797217],\n",
       "       [ 2.74511173, -0.78056359],\n",
       "       [ 0.34010583,  1.5568955 ],\n",
       "       [ 2.29180093, -0.40328242],\n",
       "       [ 1.98618025,  0.72876171],\n",
       "       [ 2.26382116, -1.91685818],\n",
       "       [ 1.35591821, -0.69255356],\n",
       "       [ 1.58471851,  0.43102351],\n",
       "       [ 1.87342402, -0.41054652],\n",
       "       [ 1.23656166,  1.16818977],\n",
       "       [ 1.45128483,  0.4451459 ],\n",
       "       [ 1.58276283, -0.67521526],\n",
       "       [ 1.45956552, -0.25105642],\n",
       "       [ 2.43560434, -2.55096977],\n",
       "       [ 3.29752602,  0.01266612],\n",
       "       [ 1.23377366,  1.71954411],\n",
       "       [ 2.03218282, -0.90334021],\n",
       "       [ 0.95980311,  0.57047585],\n",
       "       [ 2.88717988, -0.38895776],\n",
       "       [ 1.31405636,  0.48854962],\n",
       "       [ 1.69619746, -1.01153249],\n",
       "       [ 1.94868773, -0.99881497],\n",
       "       [ 1.1574572 ,  0.31987373],\n",
       "       [ 1.007133  , -0.06550254],\n",
       "       [ 1.7733922 ,  0.19641059],\n",
       "       [ 1.85327106, -0.55077372],\n",
       "       [ 2.4234788 , -0.2397454 ],\n",
       "       [ 2.31353522, -2.62038074],\n",
       "       [ 1.84800289,  0.18799967],\n",
       "       [ 1.09649923,  0.29708201],\n",
       "       [ 1.1812503 ,  0.81858241],\n",
       "       [ 2.79178861, -0.83668445],\n",
       "       [ 1.57340399, -1.07118383],\n",
       "       [ 1.33614369, -0.420823  ],\n",
       "       [ 0.91061354, -0.01965942],\n",
       "       [ 1.84350913, -0.66872729],\n",
       "       [ 2.00701161, -0.60663655],\n",
       "       [ 1.89319854, -0.68227708],\n",
       "       [ 1.13831104,  0.70171953],\n",
       "       [ 2.03519535, -0.86076914],\n",
       "       [ 1.99464025, -1.04517619],\n",
       "       [ 1.85977129, -0.37934387],\n",
       "       [ 1.54200377,  0.90808604],\n",
       "       [ 1.50925493, -0.26460621],\n",
       "       [ 1.3690965 , -1.01583909],\n",
       "       [ 0.94680339,  0.02182097]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = X_std.dot(matrix_w)\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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EFSRhEkIIIYSogiRMQgghhBBVkIRJCCGEEKIKkjAJIYQQQlRBEiYhhBBCiCpIwiSEEEII\nUQVJmIQQQgghqmC5hEkp9bBSqkgp9UIlbfq525RcnEqp82szViGEEELUDZZKmJRSVwJ3ANt9aK6B\nS4Bm7uUCrfWvJoYnhBAixKWkpJCQkBDoMExh9rb179+fAQMGmNZ/oFkmYVJKNQJSgduAIz4+7Tet\n9a/Fi3nRCSGECFaLFi3CZrOxZUvV12BVSmGzWeZPo1+ZvW1KKdP6toKwQAdQwqvAv7XWnyilZvjQ\nXgHblFIRwHfATK31V6ZGKIQQIij5+sd8/vz5FBUVmRxNYITyttUGS6TRSqkxQBfgER+f8jNwJ3AT\nMAI4AKxTSnUxJ0IhhBCh7MSJEwDY7XbCw8MDHI3viuP2RTBtm9Pp5OzZs4EOw0vAEyalVAvgJWCc\n1tqn0dFa52it/6G13qq13qC1ngR8BUw3M1YhhBC+czqdrFixgjlz5vD+++9b5g9gSkoKjRs3Ji8v\nj6SkJKKiohg/frznsdLzfJYuXUqPHj2IiooiOjqaTp06MXfu3EpfY/ny5dhsNj7//PMyj73xxhvY\nbDZ++OEHz7pdu3YxcuRIYmNjiYyM5Morr+Tf//631/OKDy2uX7+eKVOmEB8fT8uWLQH4448/uO++\n+0hISCAiIoL4+HgGDx7Mtm3bvLa79LZprXn55Zfp1KkTkZGRnH/++QwdOtTr8KXT6eSpp56ibdu2\nREREkJCQwKOPPsqZM2cqHQOA3377jUmTJtGsWTMiIyPp0qULixcv9mqzb98+bDYbL7zwAi+//LLn\ndXbs2FFl/7XJCofkugNxwBZ1bp+pHbhWKXUPUF9rrX3o52vg6qoaTZ8+nejoaK91ycnJJCcnG4ta\nCCFEhfbv38+QxER25ObS0GbjeFERCS1bsnLNGi699NKAxqaUorCwEIfDQd++fZkzZw4NGjTwPFby\n8F1GRgZjx45l0KBBPP/88wDs2LGDr776imnTplX4GsOGDaNRo0YsW7aMvn37ej22bNkyrrjiCi67\n7DIAvv/+e6655hpatGjBI488QsOGDVm2bBk33HAD7733HsOHD/d6/pQpUzj//PN54oknPHuY7rzz\nTt577z2mTp1Khw4dyM/P54svvmDHjh106dKl3G0DuPXWW1m0aBHDhg3j9ttvp7CwkM8//5wNGzbQ\nrVs3ACZNmsTixYsZNWoUf/nLX9i4cSPPPvssO3fuZPny5RWOwalTp+jXrx95eXlMnTqV1q1b8+67\n75KSksLRo0eZOnWqV/t//vOfnD59mjvvvJP69etz3nnnVdh3SWlpaaSlpXmtO3r0qE/PNURrHdAF\naAhcVmr5GlgEdDDQzxrg/6/k8W6Azs7O1kIIIc7Jzs7Wvvx+3Lx5s04aMkSHh4Xp6EaN9B133KF/\n/fXXctte06ePbh0WpjeA1qC3g+5gt+vL27fXRUVF5T6nqKhIZ2dn608//VQfPXq0xttVbOHChdpm\ns3m2LyUlRdtsNv3oo4+WaZuSkqITEhI89++77z4dExNTrdcdO3asbtasmdf2/vLLL9put+unn37a\ns27gwIG6S5cu+uzZs17Pv/rqq3X79u29tkMppfv161dmDGNiYvTUqVMrjaf0tn3yySdaKaWnT59e\n4XO2b9+ulVL6zjvv9Fr/4IMPapvNptetW+dZ179/f33dddd57r/00kvaZrPptLQ0z7rCwkJ91VVX\n6aioKP3HH39orbXeu3evVkrpmJgYnZ+fX+k2aO3b57W4DdBN+ylfCfghOa31ca31DyUX4DiQr7Xe\nAaCUekYptaj4OUqpe5VS1yul2iilLldKvQRcB8wLzFYIIURo2759O9decw37MjJ4trCQe/74g+UL\nFtD/mms4fvy4V9udO3fyRVYWcwoL6eVe1wl41enk+127yMrKKtP/li1buOLSS+nevTvXXXcdF8bH\n8/TTTxf/w2uKyZMnV9kmJiaG48ePk56ebrj/0aNH8+uvv7Ju3TrPunfffRetNaNGjQLg8OHDfPrp\np9x8880cPXqU/Px8zzJ48GByc3P5+eefPc9XSnH77beX2VMUExPDxo0bvdpWpfiw4eOPP15hm5Ur\nV6KUYvp07xkvDzzwAFprVqxYUeFzV61aRbNmzRgzZoxnnd1uZ9q0afzxxx989tlnXu1Hjhzp816l\nQAh4wlSB0t+QC4CWJe7XA+YA3wDrgI7AQK31utoITggh6pq/PfUUzc+e5WunkweAvwHrnU525uaS\nmprq1faXX34BXIcLSrq81OPFCgoKGDxwIJF79pABfA9MPnWKxx57jAULFpiwNRAWFkaLFi2qbDdl\nyhTatWtHUlISLVu2ZNKkSV7JU1FREQcPHvRaiudqDRkyhKioKN555x1P+2XLltGlSxfatm0LwO7d\nu9FaM2PGDOLi4ryWmTNnAvDrr95Vc1q3bl0mzueff57vvvuOli1b0qtXL2bNmsWPP/5Y6bbl5eVx\n4YUXEhMTU2Gb4vlFxfEWi4+PJyYmhn379lX63EsuuaTM+g4dOqC1LvPc8rbLSiyZMGmtB2it7y9x\nf6LWekCJ+/+ntb5Ea91Qax2ntR6otV4fmGiFECL0rf/0U5KdThqUWHcZ0NtmK7On4PLLLyc8LIz3\nS/XxHq49JMVzaootWbKEY0eP8rHTSaK739nASKV4wT1vyN/q16/vU7u4uDi2bdvGRx99xPDhw1m3\nbh1Dhw5l4sSJABw4cIALLriACy+80POzeA9avXr1uOGGG3j//fcpKirip59+4ssvv/Ta41J8mv9f\n/vIXMjMzyywZGRllkpXIyMgycd58883k5eUxb948mjdvzuzZs7n88surtWesPLVRY6m87bISK0z6\nFkKYLCcnhz179tC2bdty/+MToipRjRvz34ICr3VFwM9K0anUiTRxcXFMnjyZx199lSNaMxDXacz/\na7MxdvRoLr74Yq/2u3bt4rKwMJqVOovuOq15b88eE7bGmLCwMIYNG8awYcMAuOuuu3jzzTeZMWMG\nzZs3JzMz06t9586dPbdHjx7N4sWLWbt2Ld9//z2A53Ac4BmL8PDwGlfJjo+PZ/LkyUyePJlDhw7R\ntWtXnn76aRwOR7nt27Rpw5o1azhy5EiFe5latWpFUVERubm5tG/f3rP+119/5ciRI7Rq1arCeFq1\nasW3335bZn3x2W+VPdeKLLmHSQjhHwUFBQwZMoz27duTlJREu3btGDJkGIcPHw50aCLIjJ84kVSb\njQz3/ULgGeDHwkLPKfklvfDiizz48MP8vWFDHMDsiAjuuOce5v/zn2XatmnThp1OJ4dKrf8SaBPg\nP6oFpZJEgI4dOwJw+vRp6tevz4ABA7yWkmdiJyYm0qRJE5YuXcqyZcvo2bOnV6IQFxdH//79eeON\nN8ocqgQ4dKj0qJRVVFTEsWPHvNY1bdqUCy+8kNOnT1f4vJtuuomioiJmzZpVYZukpCS01rz00kte\n6+fMmYNSypNEVvTcX375xeuQpNPp5JVXXqFx48b069evqk2zFNnDJEQIGzt2ApmZG3BddehaYD2Z\nmdNITh7P6tUVT9YUorSHHnqI9Z9+yuDPPuOS8HCOac3BwkIee+wxrr66bEWXsLAwnnnmGR5//HF+\n/vln4uPjPaful/bnP/+ZJ594ghtPnmR2URHNgX8C/wJeuf/+cp9jVHUnj992220UFBQwYMAAWrRo\nwd69e5k3bx5du3alQ4cOVT4/LCyMESNGsHTpUk6cOMGcOXPKtHn11Vfp27cvHTt25Pbbb+fiiy/m\n4MGDZGVl8dNPP7F169ZKt+P333+nRYsWjBw5ks6dO9OoUSMyMjLYvHkzL7xQ4XXs6d+/PxMmTGDu\n3Lnk5OQwZMgQioqK+PzzzxkwYABTpkyhU6dO3HLLLbz55pscPnyYfv36sXHjRhYvXsyIESMqTXru\nuOMO3njjDVJSUti8ebOnrEBWVhYvv/wyDRs2rHL8rEQSJiFCVE5ODunpK3ElS+Pca8fhdGrS0yeQ\nm5srh+eEzyIjI1mzdi0rVqwgMzOTyMhIRo8e7anVU5HiQoeViYuLY2V6OmNHjaL3f/8LQHhYGP/z\nwAPcfffdfom/9BycyubklHxswoQJvPnmm7z++uscOXKEZs2akZyczBNPPOHza48ePZoFCxZgs9m4\n+eabyzzeoUMHNm/ezKxZs1i0aBH5+fmcf/75dO3atcwZbOXF3aBBA+6++27WrFnjmS/Vtm1bXn/9\nde64445Kn79w4UI6d+7MggULeOihh4iOjqZHjx5cddVVnjYLFiygTZs2LFy4kA8++IBmzZrx6KOP\nlnt2Xcn+IyIi+Oyzz3j44YdZvHgxx44do3379ixcuJAJEyaUeZ7Vr0WnzDxl00qUUt2A7Ozs7Cq/\n4EKEglWrVpGUlATsx/sk0wPARaxcuZKhQ4cGJjhhKVu2bKF79+4E+vdjYWEhX3zxBUePHqVPnz6c\nf/75AYtFWJcvn9fiNkB3rXXVV132gexhEiJEtWnTxn1rPef2MAG4zmgqfeaNEIEWFhZG//79Ax2G\nEOWSSd9ChKh27drhcCRht0/DdVjuAJCK3X4vDkeSHI4TQggDJGESIoSlpaWSmNgbmABcBEwgMbE3\naWmpVTxTCCFESXJITogQ1qRJE1avXkFubi67d++WOkxCCFFNkjAJUQdccsklkigJIUQNyCE5IYQQ\nQogqSMIkhBBCCFEFSZiEEEIIIaogCZMQQgghRBUkYRJCCCGEqIIkTEIIIYQQVZCESQghhBCiCpIw\nCSGEEG4pKSkkJCTU6mvOnDkTm83/f45r0u/ChQux2Wzs37/fz1EFL0mYhLCInJwcVq1aRW5ubqBD\nESKkLFq0CJvNxpYtVV+0XillSvISiNesSb9KKZRSfo4ouEnCJESAFRQUMGTIMNq3b09SUhLt2rVj\nyJBhHD58ONChCREyfP3jP3/+fHbu3GlyNN5mzJjBiRMnLNXvn//8Z06ePMlFF13k56iClyRMQgTY\n2LETyMzcAKQC+4FUMjM3kJw8PsCRCVFzp0+fZv/+/Zw8eTLQoVSqOLGw2+2Eh4fX6mvbbDbq1atX\naRutNadPn/Z7vxVRSlX7uaFKEiYhAignJ4f09JU4nXOBcUBLYBxO58ukp6+Uw3MiaBUWFjJjxgzi\n4uNo1aoVsXGx3H///Yb/6JshJSWFxo0bk5eXR1JSElFRUYwfP97zWOk5TEuXLqVHjx5ERUURHR1N\np06dmDt3bqWvsXz5cmw2G59//nmZx9544w1sNhs//PADUP5cI5vNxrRp0/jXv/7FFVdcQUREBOnp\n6YBrr/SECROIjo6mSZMmTJw4kW+++QabzcbixYs9fVTW74cffkjHjh2JiIjgiiuu8PRdrKI5TKtW\nraJfv36esejZsydpaWmex7/44gtGjRpFq1atiIiI4KKLLuL+++/n1KlTlY5XMJCL7woRQHv27HHf\nurbUI/0A2L17t1w0V1jGqVOnWLp0KRkZGTRo0IAxY8YwYMCAcg93PfDAA7wy7xV0bw0JcPLASV6e\n9zK/HfqNJYuXBCD6c5RSFBYW4nA46Nu3L3PmzKFBgwaex0puT0ZGBmPHjmXQoEE8//zzAOzYsYOv\nvvqKadOmVfgaw4YNo1GjRixbtoy+fft6PbZs2TKuuOIKLrvssnJfs9jatWtZtmwZ99xzD02bNqV1\n69ZorfnTn/7E5s2bmTJlCu3bt+fDDz/klltuKdNHRf1+/vnnvPfee0yZMoXGjRszd+5cRo4cyf79\n+2nSpEmFz124cCGTJk3iiiuu4K9//SsxMTFs3bqV9PR0kpOTAXj33Xc5efIkU6ZMITY2lq+//ppX\nXnmFn376iXfeeafC8QoKWus6sQDdAJ2dna2FsIpdu3ZpQEOqBl1iWaIBnZOTE+gQRR2QnZ2tq/r9\nePToUd21W1cNaHtLuw6LC9OAnjJlii4qKvJq+9tvv+mw8DDNADQzSyxJaKWUzsvLK9P/8ePH9eOP\nP65btm6pY86L0TfeeKPeunWrX7Zv4cKF2mazebYvJSVF22w2/eijj5Zpm5KSohMSEjz377vvPh0T\nE1Ot1x07dqxu1qyZ1/j88ssv2m6366efftqzbubMmdpms3k9Vymlw8LC9M6dO73WL1++XCul9Cuv\nvOK1fuDT7/iqAAAgAElEQVTAgdpms+lFixZV2W9ERIT+8ccfPeu++eYbrZTSr776qmdd8Zjt27dP\na+16/6OiovRVV12lT58+XeE2nzp1qsy65557Ttvtdn3gwIEKn2eEL5/X4jZAN+2nPEIOyQkRQO3a\ntcPhSMJun4ZrDtMBIBW7/V4cjiTZuyQs49lnn+Wb77+B28E5yUnhlEJIgtdee41PPvnEq+13331H\n4dlC6FCqk8tc/6Rv3brVa7XT6WTosKH87dm/cSD2AEc6HuGjLz6id5/eZGdnm7ZNkydPrrJNTEwM\nx48fL3PIyhejR4/m119/Zd26dZ517777LlprRo0aVeXz+/fvT/v27b3WpaenU69ePW677Tav9Xff\nfXfxzoEqDRo0iNatW3vud+zYkaioKPLy8ip8TkZGBn/88QcPP/xwpXOb6tev77l94sQJ8vPz6dOn\nD0VFRWXe92AjCZMQAZaWlkpiYm9gAnARMIHExN6kpaUGODIhzlnyryU4OzqhuXuFAq6EsLgwli5d\n6tU2Pj7edeNQqU5+c/1o1qyZ1+qPP/6Y9evWU5RcBP8fcB0473BSGFPIo4896u9NASAsLIwWLVpU\n2W7KlCm0a9eOpKQkWrZsyaRJk7ySp6KiIg4ePOi1nD17FoAhQ4YQFRXldShq2bJldOnShbZt21b5\n2iWTmmL79u3jggsuICIiwmu9L/0Va9myZZl1TZo0qfTM3OLpA5dffnmlfR84cICUlBRiY2Np1KgR\ncXFx9O/fH6UUR48e9TlGK5KESYgAa9KkCatXryAnJ4eVK1eSk5PD6tUrPHMJhLCCUydPQWSplQp0\nhC5zBlyHDh3o2bsn9gw7/Ne98lewr7bT7tJ29OnTx6t9ZmYm4XHhcHGJleHg7ORk7dq1ft8W8N4T\nUpm4uDi2bdvGRx99xPDhw1m3bh1Dhw5l4sSJgCtBuOCCC7jwwgs9P7OysgCoV68eN9xwA++//z5F\nRUX89NNPfPnll4wZM8an146MLD3g/mG328td7+seqooUFRWRmJjIqlWreOSRR/jwww/JzMxk0aJF\naK0pKiqqUf+BJpO+hbCISy65RA7BCctyDHbwzsp3cF7thOKdGz+B84CTxMTEMu3fSXuHxMGJ7Hlz\nD/ZIO86TTuJbxPPBex+UmUzcoEED9GkNTqDk3/KTEBHpvSclEMLCwhg2bBjDhg0D4K677uLNN99k\nxowZNG/enMzMTK/2nTt39twePXo0ixcvZu3atXz//fcAPh2Oq0irVq1Yt24dp06d8trLZPYZtW3a\ntEFrzXfffcfFF19cbptvv/2W3NxclixZwrhx4zzrS49PsJI9TEIgVbaFqMrjMx6nQVED7G/YYS3w\nMdgX2+nWvVu5e0xat27Nzh928uGHH/LMzGd49913+XHPj3ToUHpiE4wZM4bCY4XwBVC8E+Ig2LfY\nGZc8rkz72lRQUFBmXceOHQFXjan69eszYMAAryU6OtrTNjExkSZNmrB06VKWLVtGz549adWqVbXj\ncTgcnDlzhn/84x+edVprXn31VVMrcw8ePJjGjRvz7LPPVlgaonjPVek9SS+99FJIVA2XPUyiTiso\nKGDs2Amkp6/0rHM4kkhLS5VDYkKU0L59ezZt3MSTTz1J+pp0IhtEMn76eB555JEy82mKhYWFcf31\n13P99ddX2nfXrl15/PHHefLJJwnbHoZupHEecNL20rb87W9/80v81T3cdNttt1FQUMCAAQNo0aIF\ne/fuZd68eXTt2rXc5K+0sLAwRowYwdKlSzlx4gRz5sypVhzFbrjhBnr27MkDDzxAbm4ul156KR99\n9BFHjhwBfK9oblTjxo158cUXuf3227nyyisZO3YsTZo0Yfv27Zw8eZK33nqLSy+9lDZt2vDAAw/w\nn//8h6ioKJYvX+6JLdhJwiTqNO8q29cC68nMnEZy8nhWr14R4OiEsJb27dvzdurbpvQ9a9YsHA4H\nb7/9NkePHqVfv36MGzfOUx+ppsqrUeRL2wkTJvDmm2/y+uuvc+TIEZo1a0ZycjJPPPGEz689evRo\nFixYgM1m4+abb/Y5vvJitNlsrFy5knvvvZfFixdjs9kYPnw4M2bMoG/fvmWSV1/79eXacbfeeivx\n8fE899xz/O1vfyM8PJxLL72U6dOnA67k8OOPP2batGk899xzREREMGLECO6++26vw5TBStV0klew\nUEp1A7Kzs7Pp1q1boMMRFpCTk+M+ZTcVV5XtYqnABHJycmROkagTtmzZQvfu3ZHfj8Hrgw8+4Kab\nbuKLL74oM6k+1PjyeS1uA3TXWld91WUfyBwmUWf5UmVbCCGspvRlRoqKinjllVeIioqShNdEckhO\n1Flt2rRx31qP9x6mzwBjdU2EEKK2TJ06lZMnT9KnTx9Onz7N8uXL2bBhA88++6zP5RKEcZIwiTqr\nuMp2ZuY0nE6Na8/SZ9jt95KYKFW2hRDWNGDAAF544QVWrFjBqVOnaNu2LfPmzeOuu+4KdGghzXKH\n5JRSDyulipRSL1TRrr9SKlspdUoplaOUuqW2YhShQ6psCyGCTXJyMps2beLw4cOcPHmSb7/9VpKl\nWmCpPUxKqSuBO4DtVbRrDXwMvAaMBRKB+Uqp/2qtM0wOU4SQ4irbubm57N69m7Zt28qeJSGEEGVY\nJmFSSjXCdXrSbcCMKprfBeRprR9y39+llLoGmA5IwiQMkyrb5+Tk5LBnzx5JHoUQogQrHZJ7Ffi3\n1vqTKltCb6B0rfV0ILTPpRTCRAUFBQwZMoz27duTlJREu3btGDJkWKUX5BRCiLrCEgmTUmoM0AV4\nxMenNAMOllp3EIhSSskpAkJUg3cRz/1AKpmZG0hOHh/gyIQQIvACfkhOKdUCeAlI1FqfDXQ8QtRF\nOTk57svDlCziOQ6nU5OePoHc3Fw5PFcH7NixI9AhCFGlQH1OA54wAd2BOGCLOleX3Q5cq5S6B6iv\ny5Yj/wWIL7UuHjimtS7/qoBu06dP97owIrjOOEhOTq5u/EIEPV+KeErCFLqaNm1KgwYNGD9e9iaK\n4NCgQQOaNm0KQFpaGmlpaV6PHz161O+vGfBLoyilGgKlL928ENgBPKe1LpNKKqWeA4ZqrTuXWPcv\nIEZrnVTB68ilUYSogFwmRuzfv59Dhw4FOgwhfNK0aVMuuuiiCh8349IoAd/DpLU+DvxQcp1S6jiQ\nX5wsKaWeAZprrYtrLf0duFsp9b/AP4GBwEig3GRJCFE5KeIpLrrookr/AAlR11li0nc5Su/2ugBo\n6XlQ673AMFz1l7bhKicwSWtd+sw5IYSPpIinEEJULOB7mMqjtR5Q6v7EctqsxzX/SQjhB1LEUwgh\nKmbJhEmIUJGens7GjRvp06cPgwYNCnQ4PpEinkIIUZYkTEKYYM+ePfTqdTX5+efKhcXGxrNpUxYJ\nCQkBjEwIIUR1WHUOkxBBzZUsnaJkEcj8/FNceaUUoxdCiGAkCZMQfpaenu7es/QqrlP0W7p/ziM/\n/yAZGXK5QyGECDaSMAnhZxs3bnTfKr8IZFZWVq3GI4QQouYkYRLCz3r16uW+tb7UI58B0KePHJYT\nQohgI5O+hfAzh8NBbGw8+fl34yop5ioCCfcQGxsfNGfLCSGEOEf2MAlhgk2bsoiNjaBkEcjY2Ag2\nbZLDcUIIEYxkD5MQJkhISODQoV/IyMggKysrqOowCSGEKEsSJhGSFixYwLp16xg4cCApKSkBi2PQ\noEGWSJRycnLYs2ePVO8WIoDM/B7Kd9x8ckhOhJTs7Gzq1WvAbbfdRmpqKhMnTqRevQZs27Yt0KEF\nREFBAUOGDKN9+/YkJSXRrl07hgwZxuHDhwMdmhB1RkFBAUOShnh/D5OG+OV7aGbfwpskTCKk9OnT\nl7Nn61GyYOTZs/Xo2fOqAEcWGGPHTiAzcwMlxyMzcwPJyeMDHJkQdcfY8WPJXJ8JI3BdKn4EZK7P\nJHlcsqX7Ft4kYRIhY8GCBZw9e5LyCkaePXuShQsXBjK8WpeTk0N6+kqczrmUHA+n82XS01eSm5sb\n4AiFCH05OTmkr0rH6XBCJyAa6ATOwU7SV6XX6HtoZt+iLEmYRMhYt26d+1b5BSPXrl1bm+EE3J49\ne9y3yh+P3bt312o8QtRFnu9hq1IPtHb9qMn30My+RVmSMImQ0b9/f/et8gtGDhw4sDbDCbg2bdq4\nb5U/Hm3btq3VeISoizzfw32lHtjr+lGT76GZfYuyJGESIWPSpEmEh0cCd+Oas3PA/fMewsMjA3q2\nXCC0a9cOhyMJu30aJcfDbr8XhyNJzqQRoha0a9cOx1AH9nQ7bAeOAtvBvsaOY6ijRt9DM/sWZUnC\nJELK119/RXj4GUoWjAwPP8PXX38V4MgCIy0tlcTE3pQcj8TE3qSlpQY4MiHqjrS300i8NhHeB14E\n3ofEaxNJezvN0n0Lb0prHegYaoVSqhuQnZ2dTbdu3QIdjjDZwoULWbt2bcDrMFlFbm4uu3fvlhot\nQgSQmd9D+Y5727JlC927dwforrXe4o8+pXClCElXXXUV8fHxphzDN1IgzirF5C655BL5JSpEgJn5\nPZTvuPnkkJwIKWYWajTStxSMFEKI0CIJkwgpZhZqNNK3FIwUQojQIofkRMgoLtToSlLGudeOw+nU\npKdPIDc3t9q7rI30bWYcQgghAkP2MImQYWahRiN9S8FIIYQIPZIwiZBhZqFGI31LwUghhAg9kjCJ\nkGFmoUYjfUvBSCGECD2SMImQYmahRiN9S8FIIYQILTLpW4SUJk2asHr1ClOKuBnp28w4hBBC1D5J\nmOooqxRUNMJIzFYpECfF5ITwXTD+XhJ1hxySq2OCsaBiMMYshPBdQUEBQ5KGeH/Hk4bId1xYiiRM\ndUwwFlQMxpiFEL4bO34smeszYQQwHRgBmeszSR6XHOjQhPCQQ3J1SDAWVAzGmIUQvsvJySF9Vbor\nWerkXtkJnNpJ+vvp8h0XliF7mOqQYCyoGIwxCyF85/mOtyr1QGvXD/mOC6uQhKkOCcaCisEYsxDC\nd57v+L5SD+x1/ZDvuLAKnw/JKaW2AtqXtlrrbtWOSJimuKBiZuY0nE6Nay/NZ9jt95KYaM2CisEY\nsxDCd+3atcMx1EFmeiZO7XTtWdoL9jV2EocmyndcWIaRPUwfAB+6l3SgDXAaWOdeTrnXpfs1QuFX\nwVhQMRhjFkL4Lu3tNBKvTYT3gReB9yHx2kTS3k4LdGhCeCitfdpp5P0kpeYDP2utZ5RaPwtoqbW+\n1UBfk4G78Byx5nvgSa316gra9wM+LbVaAxdorX+t5HW6AdnZ2dl06yY7wIKxoGIwxiyE8J18x4W/\nbNmyhe7duwN011pv8Uef1T1L7magRznrU4HNgM8JE64Lbf0PkAsoIAX4UCnVRWu9o4LnaKAd8Ltn\nRSXJkigrGAsqBmPMQgjfyXdcWFl1E6aTwNW4kpySrsZ1aM5nWusVpVY9ppS6C+gNVJQwAfymtT5m\n5LVEcEtPT2fjxo306dOHQYMGVdrWaMVgK1QYtkIMQgghylfdhOkl4HX3Ya6v3et64dqz9FR1g1FK\n2YBRQAMgq7KmwDalVATwHTBTa/1VdV9XWNuePXvo1etq8vMPetbFxsazaVMWCQkJXm0LCgoYO3aC\nu3aTi8ORRFpaKk2aNCnTt9H2ZrBCDEIIISpXrbICWuvngFuA7sBc99INmOh+zBCl1BVKqd9xTSJ/\nDbhRa72zguY/A3cCN+EqdXYAWKeU6mJ4Q0RQcCVLpyhZ6Ts//xRXXtmnTFujVcGtUEXcCjEIIYSo\ngtY64AuuPV0XA12Bp4FfgUsNPH8dsKiKNt0AnZ2drUXwWL16tQY0pGrQJZYlGtBr1qzxtN21a1el\nbXNycrz6NtreDFaIQQghQk12drb7dyvdtJ9yFUtcGkVrXQjkue9uVUr1BO7FdfacL77GNX+qStOn\nTyc6OtprXXJyMsnJcs0iK9q4caP7VvmVvrOysjzzmXypCl5ybpDR9mawQgxCCBHM0tLSSEvzLkFx\n9OhRv7+OkcKVBUA7rfUhpdRhKiliqbU+r4Zx2YD6Btp3wXWorkovvviilBUIIr169XLfWs+5a8lB\ncaXvPn3OHZbzrgpetm3pisFG25vBCjEIIUQwK2+nR4myAn5jZA/TdM6dxj8dH6t+V0Up9QywCtfk\njca4/mr0Awa7H38WuFBrfYv7/r3Aj7jqNUUAtwPXAZWfNiWCksPhIDY2nvz8u3F95FyVvuEeYmPj\nvc6WM1oV3ApVxK0QgxBCCB/469hedRdgPq7DcSeBX4A1wIASj78FfFLi/oO4yhkcB34D1gLX+vA6\nMocpSOXl5enY2Pji49Ea0LGx8TovL69M24KCAu1wJHm1dTiSdEFBQbl9G21vBivEIIQQocSMOUzV\nrfS9GFe17fVa6z1VtbcCqfQd/DIyMsjKyvKpDpPRisFWqDBshRiEECIUWKnS9xngEWCBUuonXMdI\n1gGfaa1LF7MUFmRWkUQjxSWNKioq8rmt0X8EqvOPgy+MjEddqHJsZnFOI31LkVAhhGE12T0FNAeS\ngb/jqsrtBP7jr91f/lyQQ3Jaa63z8/NNOfyze/dunw+bmdm30e0LxvEIRvn5+TrJ4fAajySHwy+H\nHfPz87VjqHffjqHl922krRAieJlxSK6mSUgDXJOzn8VVmfs0sNVfwflzkYTJxeFI0nb7ee66P/s1\npGq7/TztcCTVqF9XchDt1S9E69jY+BrHbKRvo9sXjOMRjJIcDn2e3a5TQe8HnQr6PLtdJzkcNe7b\nMdSh7Q3tmhFopqMZgbY3tGvH0LJ9G2krhAhelkmYgGeAr3BN1N4CvAgMB5r4KzB/L5IwmVck0Uhx\nSTP7tkrhSjPHIxgVj3Oq92DoJe49PDUpzul5D0egmVliubFs30baCiGCmxkJU7UujQI8DLQBZgFj\ntNbTtdYfaq0PV7M/UQt8KZJYHb4Ul6wuI30b3b5gHI9gVDzO5Y9G9ce5ZN+0KvVA67J9G2krhBCl\nVTdhKr6ESU/gS6XUT0qpfyml7lBKtfNfeMKfvIskllSzIonexSXL9luyuKSZfRvdvmAcj2BUPM7l\nj0bNinN63sN9pR7YW7ZvI22FEKIMf+ymAjoDC4GzgNNfu7/8uSCH5LTWJefsLHHPrVni5zk75/r1\n/xymqvs2un3BOB7BqHgO0xL3HKYlZsxhutE9L+lGH+Yw+dBWCBG8rDSHSbkTkPuBj4ACoBD3fCZ/\nBefPRRImF7OKJBopLmlm31YpXGnmeASjgoIC086SKygo8PnMNyNthRDBy0qFKw8DjYDtnKvB9LnW\n+ojhzmqJFK70ZlaRRCPFJc3s2yqFK80cj2BkZnFOI31LkVAhQpuVCleOx5UgHauskVKqBfBfrbXv\nFQdFrTCrSOKgQYNMSwxatWpFYWEhrVu3rrKt0X8EgnE8gpGZxTmN9J2Xl8emTZsICwsLmoTJzKKw\nQoiqVSth0lqv8LHpD0AXXNeKE6JaCgoKGDt2AunpKz3rHI4k0tJSadKkSbXbirpnz5499O7Ti0O/\n5XvWNY2L5euNm0hISAhgZBXbs2cPvfr0Ir9EzLFxsWyycMxChKLqniXnK2Vy/6IOGDt2ApmZG4BU\nYD+QSmbmBpKTx9eorah7evfpxaFj+TACmA6MgEPH8unZ68pAh1ahXn16kV8q5vxj+Vxp4ZiFCEVm\nJ0xC1EhOTg7p6StxOucC44CWwDiczpdJT19Jbm5utdqKuic9Pd21Z2kY0AmIdv9MgkO/5ZORkRHY\nAMuRnp7u2rNUTsz5Fo1ZiFAlCZOwNCPFJc0qRClCg6egaAWFK61YUDQYYxYiVEnCJCzNSHFJswpR\nitDgKShaQeFKKxYUDcaYhQhVZidMxmsWCFFCu3btcDiSsNun4ZqXdABIxW6/F4cjyesMJyNtRd3j\ncDhoGhcLK3AVRDnq/rnSNfHbimeeORwOYiuIOdaiMQsRqmTSt7C8tLRUEhN7AxOAi4AJJCb2Ji0t\ntUZtRd3z9cZNNI2KhfdxXTL8fWga5TpLzqo2bdxEbKmYY6NcZ8kJIWpPtQpX+ty5Ui1x1WFymvYi\nvscihSuDnBQmFP4SjAVFgzFmIQIloIUrlVLv+dpWaz3C/fNAdYISLjk5OezZs8eUP/pG+rZKwTwj\nyb2ZBRKFNzM/p2YxUlDU6OffrPEwErPRGIy0t8rvJSFqna/XUAHe8nXx13Vb/LkQRNeSy8/PN+X6\nZkb73r17tyWuh2bmeIjqy8/PN+36cFawe/du3TQu1mv7msbFVvj5z8/PD/h16ozGYKS9me+3FcZO\nhBbLXHw3GJdgSpgcjiRtt5+nIdV9pftUbbefpx2OpFrt25UsRXu1hWgdGxtf4zjMilnUniSHQ59n\nt+tU0PtBp4I+z27XSQ5HoEPzi6ZxsZr6aEagme7+Wd+VNJXHMdSh7Q3tXu3tDe3aMbT2xsNoDEba\nm/l+W2HsRGiRhKkOJEy7du1yv8mpGnSJZYkGdE5OTq30vXr16krbrlmzxh+b69eYRe0pfl9Svd8U\nvcS9dyDY3xfP538Empkllhsp9/Pv+ZxW0L42xsNoDEbam/l+W2HsROgxI2Gq9llySqmRSqllSqkN\nSqktJZfq9inMLb5opG9PwbwK2tZWwTwpRmlNxe9L+e9K8L8vRgtGej6nFbSvjfEwGoOR9ma+31YY\nOyF8Ua2ESSk1Ddd8pYNAV+BrIB+4GFjlt+jqIDOLLxrp21Mwr4K2tVUwT4pRWlPx+1L+uxL874vR\ngpGez2kF7WtjPIzGYKS9me+3FcZOCJ9UZ7cUsBNIdt/+HbjYfftJYJ6/dn/5cyFIDslpXXLOzhL3\nnJ0lJsxhqrrvc3OYzrUN7Bwm/4+HqL7iOS1L3HNaloTqHKYb3fNqbvRxDlOJ9gGbw+RjDEbam/l+\nW2HsRGixzBwm4ATQyn37V6Cz+/YlQL6/gvPnEkwJU0FBgWlnhRnpOy8vzxJnyZk5HqL6CgoKQvos\nuby8PENnyRUUFAT8TC+jMRhpb+b7bYWxE6HFjISpWoUrlVJ5wE1a661Kqc3AP7TWbyilBgNLtdbn\nGe7UZMFYuNLM4otG+rZKwTwpRmlNof6+GP38W2E8jMZglaKwVhg7ERrMKFxZ3YRpPnBAaz1LKXU3\n8H/Al0AP4D2t9SR/BOdPwZgwmcmsAnFG+12wYAHr1q1j4MCBpKSk+C0OIfzFKsUUzfyuyPdQhBoz\nEqbqHt6yAWEl7o8B5gJTgXr+2v3lz4UgOiRnJrOKQBrtd/PmzTo8PNKrfXh4pN66dWuN4hDCX6xS\nTHHz5s26Xr1wrzjq1Qv3y3dl8+bNOry+d9/h9f3TtxCBZJlDcsFI9jC5DBkyjMzMDTidc3GdJLwe\nu30aiYm9Wb16Ra31W69eA86erQe86mkPdxMefoYzZ05UOw4h/GVI0hAy12fidDhdp7zvA3u6ncRr\nE1m9cnWtxVG/fj3OqLMwDE8crIB6OpzTp8/UqO96EfU4S9m+wwnnzKma9S1EIJmxh6kmdZiaKKX+\nopRa4F4eUEpZbu6SOCcnJ4f09JXupGYc0BIYh9P5MunpK8nNza2VfhcsWMDZsydxJUvn2sM8zp49\nycKFC6u7iUL4RU5ODumr0l3JUicgGugEzsFO0lelV/u7YtSCBQs4c8ad0JSIgyQ4c+Zsjb4rCxYs\n4Ozp8vs+e7pmfQsRiqpbh+la4EdgGtDEvUwDfnQ/JizIrCKQRvtdt25dpe3Xrl1brTiE8BerFFP0\nfFcqiKMm3xUz+xYiFFV3D9OrwDIgQWs9Qms9AlfRyqXux4QFmVUE0mi//fv3r7T9wIEDqxWHEP5i\nlWKKnu9KBXHU5LtiZt9ChKLqniV3Euiitd5Van17YJvWOtJP8fmNzGFyOTfX6GVce3Q+w26/149z\nmHzr99wcpnme9nCPzGESluGZwzTY6drrshfsawI4hykJTxys9PMcplJ9yxwmEeysdJbcl8AN5ay/\nAdjgrxnp/lyQs+S01uYVgTTa79atW+UsOWFpVimmuHXrVtPOktu6daucJSdCkmXOklNKjQaeB14B\nNrhX9wbuBh4GdpRIyL6poq/JwF14jpzzPfCk1rrCf+GUUv2BOcDlwH7gaa31oipeR/YwlWBWgTij\n/S5cuJC1a9dK/RdhWVYppmjmd0W+hyLUWKlwZVEVTTSgAK21tlfR1zDACeS6n5MCPIjrkN+Octq3\nBr4DXgMWAInAS0CS1jqjkteRhEkIIYSoA8xImMKq+bwEf7w4gNa69ASXx5RSd+HaY1UmYcK1NypP\na/2Q+/4updQ1wHSgwoTJKoxUDbZKhWEj0tPT2bhxo8+XkQj18TAzZiNjHYxxGP0sWYWRqtlmjYdV\nvitG47BK3EaE+u8wUYK/ju35Y8F11t4Y4CRwaQVtPgNeKLUuBThcRd8BncNkpBK2WdW4zbR7925D\nF+oN9fHIz8837UKlu3fv1vGx3heFjY8t/6KwZsfh68VpjVTNNtKvlRipmm3kfTFrnM1kNA6rxG2E\nkZiDcfuCnRlzmGqS3EzANfn7v0Ar97r7gOHV6OsK4HfgLFAADKmk7S7gf0qtG4rrsF79Sp4X0ITJ\n4UjSdvt5GlI17NeQqu3287TDkVSjtlbhSpaivWKGaB0bG19u+1AfjySHQ59nt+tU0PtBp4I+z27X\nSQ5HjfuOj43V0e4+i/uOdidNtRlH07hYTX00I9BMd/+s7/pjXppjqEPbG9q92tob2rVjaNk4jPRr\nJeH1w8uNO7x+eJm2Rt4Xs8bZTEbjsErcRhiJORi3L9hZJmHCdVjsN+BR4ARwsT63p+fTavQXhquO\nU1fgaeDXSvYwBV3CtGvXLvcbl6pBl1iWaEDn5ORUq61VrF69utKY16xZ49U+1MejOOZU74D1Evd/\nljWJuXisK+q75FjXRhyMQDOzxHJjxXFU1LZkHEb6tZL58+dXGvdbb73laWvkfTFrnM1kNA6rxG2E\nkcg3h9MAAB+2SURBVJiDcftCgRkJU3ULV04FbtdaP+1OVIptBjoa7UxrXai1ztNab9VaPwpsB+6t\noPkvQHypdfHAMa316apea/r06Vx//fVeS1pamtGQDTFSCdusatxm2rhxo/tW+TFnZWV5rQ318SiO\nufyIaxZz8VhX1HfJsa6NOCqqEl1eHL5UzTbSr5UYqZpt5H0xa5zNZDQOq8RthJGYg3H7gk1aWlqZ\nv+vTp0/3++vUZNL31nLWnwYaVj8cDxtQv4LHsnDtUSppsHt9lV588cVaP0vOuxL2uBKPlK2EbaSt\nVfTq1ct9q/yY+/Tp49U+1MejOObyI65ZzMVjXVHfJce6NuJgH67rjxXbW3EcFbUtGYeRfq2kf//+\npKamVhh3yarZRt4Xs8bZTEbjsErcRhiJORi3L9gkJyeTnJzsta7EWXL+U53dUsAPuOcq4Zp7VHxI\nbiqwxWBfzwB9ceXfVwDPAoXAAPfjzwKLSrRv7X7N/wXaA1OAM0BiFa9jkTlMS9zzcJb4MGen6rZW\ncW4O07mYfZvDFJrjUTxHZYl7jsoSE+Ywley7qjlMZsThmVtzo3texo0+zK0p0bbKOUw+9GslnjlM\npeKubA6TL++LWeNsJqNxWCVuI4zEHIzbF+ysNIfpNuA/wGjgD1xntj1afNtgX/OBPFxnxv0CrClO\nltyPvwV8Uuo51wLZ7ufkAhN8eJ2AJkxGKmGbVY3bTHl5eYbOkgv18SgoKDDt7LS8vDyfz5IzOw5f\nz94yUjXbSL9WYqRqtpH3xaxxNpPROKwStxFGYg7G7Qt2lqn0DaCUGgfMBIqPmfwEzNRaL6hWhyaz\nSuFKI1WDrVJh2IiMjAyysrJ8rp0T6uNhZsxGxjoY4zD6WbIKI1WzzRoPq3xXjMZhlbiNCPXfYcHK\nSpW+I93PPaGUaoDrUNrVwA9a63R/BOZvVkmYjJAiZ6IywVgwz8yYzerbzLGzyvsSjGTsRGWsdPHd\nNcBk9+0YXIfSDuA6RHaXv3Z/+XMhiC6+G4yFGkXtMVL00MzClVaJ2WjfRooNmjV2VnlfgpEUgRS+\nsNIcpkPA5frcfKbtuM5suxnY4a/g/LkEU8IUjIUaRe0xUvTQzMKVVonZSHsjBQTNHDurvC/BSIpA\nCl9YKWE6AVzkvr0MeMJ9uyVwwl/B+XMJloQpGAs1itpjpOihmYUrrRJzdfo2UmzQjLGzyvsSjKQI\npPCVlQpX7gZuUEq1BBy4DtEBnA8cq2afguAs1Chqj5Gih2YWrjTCzJir07eRYoNmjJ1V3pdgJEUg\nRSBVN2F6EpiNq/TWRq11cdHIwZRf0FL4yLtQY0nWLdQoak/JoocllVf00EhbM5kZc3X6Zl+pxntr\nHocRVnlfgpGR91AIv6vurimgGa5rv9lKrOtJBdeAC/RCkByS0zo4CzWK2mOk6KGZhSutErOR9kYK\nCJo5dlZ5X4KRFIEUvrDMHKZgXIIpYQrGQo2i9hgpemhm4UqrxGy0byPFBs0aO6u8L8FIikAKX1iq\ncGWwCcY6TFLkTFQmGAvmmRmzWX2bOXZWeV+CkYydqIwZdZiqe/FdUQsuueQS+UUgKmTkn528vDw2\nbdpEWFhYQAs1GvlMG4kZjI2HWWMHxsbPrO94XSjqaGTsrDIeVolDVJO/dlVZfSGIDskJURkjhft2\n797t83XnrFIQ0EjMRuM2Onaxpa7hFlvJNe2sUIzSCjFYiVU+01aJoy6ROUySMAlhqHBffGysjnbX\n/CkukBjtTkBq0q+ZjMRsNG4jbWPjYjX18WpLfVfSVB4rFKO0QgxWYpXPtFXiqEskYZKESdRxRgr3\nrV69utICiWvWrKlWv2YyErPRuKszdhW1rSiOQBajtEIMVmKVz7RV4qhrrFS4UggRAEYK923cuBGo\nuEBiVlaWZ51VCgIaiRmqV4zSyNhV1LaiOAJZjNIKMViJVT7TVolD1JwkTEIEESOF+3r16gVUXCCx\nT58+1erXTEZihuoVozQydhW1rSiOQBajtEIMVmKVz7RV4hB+4K9dVVZfkENyIkQYKdxXPB+oZIHE\nKucwBbggoJGYjcZtpK1nDlOJtr7MYQpkMUorxGAlVvlMWyWOukTmMEnCJIShwn15eXk+n3FmlYKA\nRmI2GrfRsTNylpwVilFaIQYrscpn2ipx1CVSuLIGgrFwpRCVMVK4LyMjg6ysLPr06cOgQYP81q+Z\njMQM5hWjNDMOs1ghBiuxynhYJY66QApXCmGSYCwoZ+SfnUGDBvn0xx5g2bJlrF27lsGDB/Pwww9X\nN7xyGRlnIzGDsUKGRsauVatWFBYW0rp1a7/HYRYrxGAlVhkPq8Qhqslfu6qsviCH5EQ5grHQn1kx\nr127Viub8upX2ZT+7LPPLBtzdeIwo8ilEMJaZA6TJEzCz4Kx0J9ZMSubKrdQo7Ipy8ZslFlFLoUQ\n1iJ1mITwo5ycHFampzPX6WQc0BIYB7zsdLIyPZ3c3NwAR1iWWTH/v/buPsquujz0+PdhqGBpwTTB\nqPfyEkMmrbaFgpAEBLSEnDAsbaVdrQnk+lJsfemFlb54ddmuW1evSq3lRdF729rFVaeOt/euS6sl\ncJLBF644CSAFq1AnL/KiQiAzNCCIyuR3/9hn4plxZvacM+dl7zPfz1pnzcw+e+/zPPt3knlm7995\n9vve9z7SoQQXA78MHFf7OgDpUOKqq64qXMzNxFG9ucpEZWJKjhMbJqjePDWORtaVtDhYMGnRKmOj\nv3bFfOutt2bfzNJcb/v27U3tF4pznNvV5FLS4mDBpEWrjI3+2hXzBRdckH0zS3O9DRs2NLVfKM5x\nbleTS0mLgwWTFq3+/n4GKhWu6OtjEHgYGASu7OtjoFIp5KdZ2hXze97zHuKIgJuAe4GDta/bII6I\nBX1arijHub+/n8pFFfqqfVNy7NveR+WiqXE0sq6kRaJVk6GK/sBJ35pBGRv9tSvmL33pS237lFxR\njnO7mlxKKhYbVy6AjSs1lzI2lGtXzFdddRXbt29vSx+mohzndjW5lFQM7WhcacEkFUQ7m2c2su+i\nNPEsShxSq/ie7px2FEzOYZK6bHx8nIs3bmT16tUMDAzQ39/PxRs38sQTT3R03+2Mo9GYNw5MjWPj\nQOfjkFrF93RvsGCSumzL5s3sHB5mEHiIbEL0zuFhLtu0qaP7bmccjdh82WaGbxuGS4CtwCUwfNsw\nmy7tbBxSq/ie7g1ekpO6aHR0lNWrVzNI1sxx0iCwpfZ8s6fuG9l3O+NoJmYuIWsYOele4MbOxSG1\niu/p7vCSnNRj2tnUsZF9l7G5pFQGvqd7hwWT1EXtbOrYyL7L2FxSKgPf073DgknqonY2dWxk32Vs\nLimVge/pHtKqhk5Ff2DjShVUO5s6NrLvMjaXlMrA93Tn9WTjyoh4N/A64OeB7wNfAf5LSml0jm3O\nB74wbXECXpxSemyWbZz0rUJrZ4PEMjZqLEocUqv4nu6cdkz6PrIVO1mgc4GPAHeRxfMBYHtE/EJK\n6ftzbJeAfuCpwwtmKZZUXO1q5FbGBnGN/vHSSI6N7HvVqlWFOGbtjKNarbJr1y7WrVvHhRde2JbX\naLUyvqc1VVH+balJrTpV1aoHsAw4BLxyjnXOByaAYxvYr5fkCmRsbKwtp6jHxsYKcVmpEY3G3Mj6\n7TrOZbVnz5607PilU47HsuOXpn379nU7tFk5hlLj2nFJrusF0k8EBKfUiqGXzbHO+bWiah/wXWA7\ncHbOfi2YCqRyUSX1HdOXuITEVhKXkPqO6UuViyoL2u9ApZJ+rq8vDUJ6CNIgpJ/r60sDlYXtt50a\njbmR9dt1nMtq2fFLE0cx5XhwVFY0FZVjKDWuJ+cw1YuIAD4H/GxK6fw51usnK5ruAo4C3kLWX++s\nlNI9s2zjHKaCaFcjt6I0X2xEozE304zShnmZarXKxo0bZz0e27dvL9zlOcdQak6vzmGq9zHgZcA5\nc62Usgnh9ZPCd0bESrKm82+Ya9utW7dy3HHHTVm2adMmNnX49g+L2XwauTXzS2A+zReL9sul0Zgb\nWb9dx7msdu3alX0zy/EYGRkpXMHkGEr5hoaGGBoamrLs4MGDLX+dwhRMEXE9MACcm1J6pIld3EFO\noQVwzTXXeIapy6Y0cqv/q/mB7Euzjdzqmy/Wn33pdPPFRjQacyPrt+s4l9WaNWuyb2Y5HuvWretw\nRPkcQynfTCc96s4wtUwhGlfWiqVfA16dUnqoyd2cBjRTaKnD2tXIrSjNFxvRaMyNNqO0Yd6PVSoV\nlh2/FG5iyvFgGyw7fmnhzi6BYygVSqsmQzX7ILsM9wRZe4HldY+j69Z5P/CJup+vBF4LrAReDlwL\n/Ah41Ryv46TvAmlXI7eiNF9sRKMxN9qM0k9Y/di+fftK9yk5x1BqXE9O+o6IQ7WkpntTSumTtXVu\nAE5KKf1q7ec/Bn4XeAnwDPA14L0ppem3wqp/HSd9F1C7GrmVsUFcozGXsRllUezYsYORkZFS9WFy\nDKX5a8ek764XTJ1iwSRJ0uKwGD4lp0XG7sU/Vsbu05K0WFgwqSvGx8fZsnkz26rVw8sGKhUGh4ZY\nsmRJFyPrvL1793LOmjXsHxs7vGz50qWM3HknK1as6GJkkqRJhfiUnBafLZs3s3N4mEHgIbJPeu0c\nHuayRdgP65w1a3h2bGzKsXh2bIx1Z57Z5cgkSZMsmNRxo6OjbKtW+fDEBJcCJ5D1FLpuYoJt1Sq7\nd+/ucoSdU61W2T82xkdhyrG4Htg/NsaOHTu6Gp8kKWPBpI6bT7fqxWKy+/Rsx2JkZKSj8UiSZmbB\npI6r71Zdr8gdudtlsvv0bMeiiN2nJWkxctK3Ou5wt+rhYdLEBOeTFQhX9vUxsH79ovq0XKVSYfnS\npbxjbIwEh4/F75NN/PbTcpJUDJ5hUlcMDg2xdv16tgAnAluAtevXMzjtBoqLwcidd3L00qVTjsXR\ntU/JSZKKwTNM6oolS5Zw0y232L0YWLFiBY8eOFDK7tOStFhYMPWIsjaAXLVq1bzjLWuO83XSSSfx\n3HPPcfLJJ3c7lJ7X6+8lSa3nJbmSGx8fZ+PGi1m9ejUDAwP09/ezcePFPPHEE90OrWXGx8e5eOPG\nKTlevHFjz+TY6/kVicdaUrMsmEpu8+YtDA/vhLq2h8PDO9m06bIuR9Y6vd7kstfzKxKPtaRmefPd\nEhsdHWX16tVk/+1fWvfMILCF0dHR0l9umMxx5gwpfY69nl+ReKylxaMdN9/1DFOJTTaAnK3tYS80\ngOz1Jpe9nl+ReKwlLYQFU4lNNoCcre1hLzSA7PUml72eX5F4rCUthAVTifX391OpDNDXdwXZhYWH\ngUH6+q6kUhnoicsLh5tc9vXVZVhrclmplD7HXs+vSDzWkhbCgqnkhoYGWb9+LdS1PVy/fi1DQ4Nd\njqx1er3JZa/nVyQea0nNctJ3j1gMDSB7Pcdez69IPNZSb2vHpG8LJkkdU61W2bVrV1u6mduMUtKk\ndhRMdvqW1HZ79+5l7bo1HHh87PCyZccv5Y5dd7JixYoF7Xt8fJwtmzezrVo9vGygUmFwaIglS5Ys\naN+SNMk5TJLabu26NRx4cgwuAbYCl8CBJ8c4a82ZC963zSgldYIFk6S2qlar2Zmli4FfBo6rfR2A\nA4+PsWPHjqb3PTo6yrZqlQ9PTHApcAJZU8rrJibYVq2ye/fuVqQgSRZMktpr165d2TcnTXvi5OzL\nyMhI0/u2GaWkTrFgktRWa9asyb55cNoTD2Rf1q1b1/S+bUYpqVMsmCS1VaVSYdnxS+Em4F7gYO3r\ntmzi90I+LWczSkmdYsEkqe3u2HUny45dCjcC1wA3wrJjs0/JLZTNKCV1gm0FJLXdihUrePyxA+zY\nsYORkZGW9mFasmQJN91yi80oJbWVBZOkjrnwwgtb3rBy0qpVqyyUJLWNl+QkSZJyWDBJkiTlsGCS\nJEnKYcEkSZKUw4JJkiQphwWTJElSDgsmSZKkHF0vmCLi3RFxR0Q8GRH7I+LGiOifx3avioivRsSz\nETEaEW/oRLySJGnx6XrBBJwLfARYA6wHfgrYHhHPn22DiDgZ+GfgVuBU4Drg4xHRno54KoTR0VFu\nvvlmdu/e3e1QJEmLTNc7faeUBup/jog3Ao8BZwBfnmWztwH7UkrvrP38zYh4JbAV2NGmUNUl4+Pj\nbNm8mW3V6uFlA5UKg0NDLFmypIuRSZIWiyKcYZruBUACxudYZy0wPG1ZFVjXrqDUPVs2b2bn8DCD\nwENkd6PfOTzMZZs2dTkySdJi0fUzTPUiIoBrgS+nlO6bY9UXAfunLdsPHBsRR6WUftCuGNVZo6Oj\nbKtWGQQurS27FEgTE2ypVtm9e7f3D5MktV2hCibgY8DLgHPa9QJbt27luOOOm7Js06ZNbPJsRSHt\n3bsXgPOmLT+/9nXPnj0WTJK0iA0NDTE0NDRl2cGDB1v+OoUpmCLiemAAODel9EjO6o8Cy6ctWw48\nmXd26ZprruH0009vPlB11MqVKwG4jR+fYQL4Uu3rKaec0umQJEkFMtNJj7vvvpszzjijpa9TiDlM\ntWLp14BXp5QemscmI8AF05ZtqC1XD+nv72egUuGKvj4GgYfJ5jBd2dfHQKXi2SVJUkd0vWCKiI+R\nnTzYDDwdEctrj6Pr1nl/RHyibrP/Abw0Iv4iIlZHxNuB3wSu7mjw6ojBoSHWrl/PFuBEYAuwdv16\nBqedgpUkqV2KcEnurWSfivvitOVvAj5Z+/7FwAmTT6SUHoiIi4FrgCuAbwO/k1Ka/sk59YAlS5Zw\n0y23sHv3bvbs2cMpp5zimSVJUkd1vWBKKeWe5UopvWmGZbeR9WrSIrFq1SoLJUlSV3T9kpwkSVLR\nWTBJkiTlsGCSJEnKYcEkSZKUw4JJkiQphwWTJElSDgsmSZKkHBZMkiRJOSyYJEmSclgwSZIk5bBg\nkiRJymHBJEmSlMOCSZIkKYcFkyRJUg4LJkmSpBwWTJIkSTksmCRJknJYMEmSJOWwYJIkScphwSRJ\nkpTDgkmSJCmHBZMkSVIOCyZJkqQcFkySJEk5LJgkSZJyWDBJkiTlsGCSJEnKYcEkSZKUw4JJkiQp\nhwWTJElSDgsmSZKkHBZMkiRJOSyYJEmSclgwSZIk5bBg6iFDQ0PdDqHtej3HXs8PzLEX9Hp+0Ps5\n9np+7VCIgikizo2Iz0bEdyLiUES8Nmf982vr1T8mIuKFnYq5iBbDP4Bez7HX8wNz7AW9nh/0fo69\nnl87FKJgAo4B7gHeDqR5bpOAVcCLao8Xp5Qea094kiRpMTuy2wEApJRuAW4BiIhoYNPHU0pPticq\nSZKkTFHOMDUjgHsi4rsRsT0izu52QJIkqTcV4gxTEx4Bfg+4CzgKeAvwxYg4K6V0zyzbHA1w//33\ndybCLjh48CB33313t8Noq17PsdfzA3PsBb2eH/R+jr2eX93v+qNbtc9Iab5ThjojIg4Bv55S+myD\n230ReDCl9IZZnt8M/P3CI5QkSSVxaUrp063YUVnPMM3kDuCcOZ6vApcCDwDPdiIgSZLUFUcDJ5P9\n7m+JXiqYTiO7VDejlNIY0JIqU5IkFd5XWrmzQhRMEXEMcArZRG6Al0bEqcB4SunhiPgA8JLJy20R\ncSXwLeAbZFXkW4BXAxd2PHhJktTzClEwAa8AvkDWWykBf1Vb/gngzWR9lk6oW/95tXVeAjwDfA24\nIKV0W6cCliRJi0fhJn1LkiQVTZn7MEmSJHVEzxVMEfGu2r3lrs5Z71UR8dWIeDYiRiNixnYERTSf\nHMt2v72I+K8zxHtfzjalGcNG8yvb+E2KiJdExKci4kBEPBMR90bE6TnblGkcG8qvbOMYEd+aId5D\nEfGRObYpzfhB4zmWcAyPiIg/j4h9tffonoj4k3lsV5pxbCbHVoxjUeYwtUREnAn8LnBvznonA/8M\nfAzYDKwHPh4R300p7WhzmAsy3xxrEtAPPHV4QbHvt/d14AJ+PPn/udlWLOkYzju/mlKNX0S8ALgd\nuBWoAAfI7vf4xBzbnExJxrGZ/GrKNI6vAPrqfv4lYDvwDzOtXKbxq9NQjjVlGsN3kTV2/k/AfWT5\n/s+I+PeU0vUzbVDCcWw4x5oFjWPPFEwR8TPAIHA58Kc5q78N2JdSemft529GxCuBrUAR3xxAwzlO\nKtP99p5LKT0+z3XLOIaN5DepTOP3LuChlNLldcsezNmmTOPYTH6TSjGOtfYrh0XEa4C9KaX/N8sm\nZRo/oKkcJ5ViDIF1wD/V7tEK8FBkjZvPmmObso1jMzlOanoce+mS3EeBz6WUPj+PddcCw9OWVckG\nocgayRHKd7+9VRHxnYjYGxGDEXHCHOuWcQwbyQ/KN36vAe6KiH+IiP0RcXdEXJ6zTZnGsZn8oHzj\nCEBE/BRZs9+/m2O1Mo3fT5hnjlCuMfwKcEFErAKIrEXPOcC2ObYp2zg2kyMscBx7omCKiNeTNa58\n9zw3eRGwf9qy/cCxEXFUK2NrlSZynLzf3m8AlwAPk91v77T2RLhgO4E3kl3qeCuwArgtsh5dMynb\nGDaaX9nGD+ClZH+pfhPYAPx34MMRsWWObco0js3kV8ZxnPQ64Diy9i6zKdP4zWQ+OZZtDK8C/hfw\nbxHxQ+CrwLUppc/MsU3ZxrGZHBc8jqW/JBcR/xG4FlifUvpRt+Nph2ZyTCmNAqN1i3ZGxEqyU6yF\nm8yXUqpvX//1iLiD7HLHbwE3dCeq1mk0v7KNX80RwB0ppcnLxfdGxC+SFYif6l5YLdNwfiUdx0lv\nBm5OKT3a7UDaKDfHEo7hb5PNQ3o92fye04DravOReuHfITSRYyvGsfQFE3AGcDxwd0RMTqbtA86L\niN8Hjko/2WzqUWD5tGXLgSdTSj9oa7TNaSbHmeTdb68wUkoHI2KUrAP8TMo2hlPMI7+ZFH38HgHu\nn7bsfrK/5mZTpnFsJr+ZFH0ciYgTySb+/nrOqmUavykayHEmRR7DDwIfSCn979rP36hN6n43s//h\nUrZxbCbHmTQ0jr1wSW6Y7FMOpwGn1h53kU2OPnWWQmKE7NNK9TbUlhdRMznOZM777RVJbYL7Kcwe\nb9nGcIp55DeToo/f7cDqactWM/fE6DKNYzP5zaTo4wjZmZf95M8JKdP4TTffHGdS5DH8aWBi2rJD\nzP37vmzj2EyOM2lsHFNKPfcgu83K1XU/vx/4RN3PJ5N9rPAvyP7DezvwQ7JLXl2Pv0U5Xgm8FlgJ\nvJzskt6PgFd1O/ZZ8vlL4DzgJOBssk9m7AeW9sIYNpFfqcavFvMrgB+Q/ZW3kuyU+VPA6+d4n5Zm\nHJvMr4zjGMADwPtmeK6047eAHEs1hmSX+B8CBmr/37wOeAx4f6+MY5M5Lngcu554mw7m55laTNwA\nfH7aOueRTRT7PrAb2NLtuFuZI/DHtbyeBh4n6x1zXrfjniOfIeDbtfF4CPg0sKJXxrDR/Mo2fnVx\nD5Dd2/EZsptjv3na82Ufx4byK+M4kt3EfAI4ZYbnSj1+zeRYtjEEjgGuJrtB/dO12N8LHNkr49hM\njq0YR+8lJ0mSlKMX5jBJkiS1lQWTJElSDgsmSZKkHBZMkiRJOSyYJEmSclgwSZIk5bBgkiRJymHB\nJEmSlMOCSZIkKYcFk6SeFxE3RMT/nee6X4iIq9sdk6RysWCSJEnKYcEkSZKUw4JJUttFxG9GxNci\n4pmIOBAR2yPi+bXnLo+I+yLi+7Wvb6vb7qSIOBQRvx0Rt9fW+deIOK9unSMi4uMRsa+2/3+LiCta\nGPvzIuJDEfHtiPheRIxExPl1z78hIp6IiA21+J+KiJsjYnmrYpDUfUd2OwBJvS0iXgR8Gvgj4B+B\nnwXOzZ6KS4E/A94B3AP8CvC3EfG9lNKn6nbzQeBK4H7gD4HPRsSKlNITZH/4PQz8BjAOnA38TUR8\nN6X0f1qQwkeBnwd+C3gEeB1wc0T8Ukppb22dn67FdSmQgL8HPgRsacHrSyoACyZJ7fZioA+4MaX0\ncG3ZNwAi4s+AP0wp/VNt+YMR8XLgrUB9wfSRlNI/1rZ5G7AR+B3gQyml54D31q37YEScTVbgLKhg\niogTgTcCJ6SUHq0tvjoiLgLeBPxJbdmRwO+llB6obXc98KcLeW1JxWLBJKnd7gVuBb4eEVVgO1kh\n80NgJfB3EfHxuvX7gH+fto+dk9+klCYi4i7gFyaXRcQ7yAqYE4HnA88D/qUFsf9iLZ7RiIi65c8D\nDtT9/MxksVTzCPDCFry+pIKwYJLUVimlQ8CGiFgHbAD+M/DfgNfWVrkcuGPaZhPz3X9EvB74S2Ar\nWWH1FPBO4KyFRQ7AzwDPAacDh6Y9972673807bkEBJJ6hgWTpI5IKY0AIxHx58CDwDnAd4CVKaXP\n5Gy+FvgyQET0AWcAH649dzZwe0rprydXjoiVLQr7X8jOMC1PKd3eon1KKiELJkltFRFnAReQXYp7\njKz4WQbcRzbh+7qIeBK4BTgKeAXwgpTStXW7eUdE7CGb9P0HwAuAG2rP7Qa2RMQG4FtkE63PBPYt\nNPaU0u6I+DTwyYj4I7IC6oXArwL3ppRuXuhrSCoHCyZJ7fYkcB7Zp9yOJTu79AcppSpARDxNdgnt\ng8DTwL8C107bx7tqj1OBPcBrUkrjtef+GjgN+AzZpbAhsk+2XdRkvGnaz28km9z9IeA/kM1d2gl8\nrsn9SyqhSGn6/w2SVAwRcRLZmaJfSSl9rdvxSFq8bFwpqeicPC2p67wkJ6noWnYaPCJOIJs7NdOn\n2BLwspTSt1v1epJ6h5fkJC0atU/YnTTHKg/U2iBI0hQWTJIkSTmcwyRJkpTDgkmSJCmHBZMkSVIO\nCyZJkqQcFkySJEk5LJgkSZJyWDBJkiTlsGCSJEnK8f8B9sfv/wqSqaEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x261b6e45588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "for lab, col in zip(('Iris-setosa', 'Iris-versicolor', 'Iris-virginica'),\n",
    "                        ('blue', 'red', 'green')):\n",
    "     plt.scatter(X[y==lab, 0],\n",
    "                X[y==lab, 1],\n",
    "                label=lab,\n",
    "                c=col)\n",
    "plt.xlabel('sepal_len')\n",
    "plt.ylabel('sepal_wid')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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si7hQlcAJmIszbLcMmFvodcq9KXCqfdlm1CT2L1++omTr4ElzS8zqXAn2yHjw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\nf0QSo+4j8xxiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x261baea04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "for lab, col in zip(('Iris-setosa', 'Iris-versicolor', 'Iris-virginica'),\n",
    "                        ('blue', 'red', 'green')):\n",
    "     plt.scatter(Y[y==lab, 0],\n",
    "                Y[y==lab, 1],\n",
    "                label=lab,\n",
    "                c=col)\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
